therapeutics and covid-19

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Therapeutics and COVID-19

L I V I N G G U I D E L I N E2 0 N O V E M B E R 2 0 2 0

© World Health Organization 2020. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO licence. WHO reference number: WHO/2019-nCov/remdesivir/2020.1

Therapeutics and COVID-19

LIVING GUIDELINE 20 NOVEMBER 2020

https://creativecommons.org/licenses/by-nc-sa/3.0/igo

Therapeutics and COVID-19 World Health Organization (WHO)

1. SUMMARY: WHAT IS THIS LIVING GUIDELINE?

Clinical question: What is the role of drugs in the treatment of patients with COVID-19?

Target audience: The target audience is clinicians and health care decision-makers.

Current practice: Current practice to treat COVID-19 is variable, reflecting large-scale uncertainty. Numerous randomized controlled trials (RCTs) of many different drugs are underway to inform practice. This version of the World Health Organization (WHO) living guideline contains new information and recommendations on remdesivir. It follows the preprint publication of results from the WHO SOLIDARITY trial on 15 October 2020 (1), which also reported results on hydroxychloroquine and lopinavir-ritonavir. Remdesivir is increasingly used to treat patients hospitalized with COVID-19 and is of considerable interest to all stakeholder groups.

Recommendations: The panel made a conditional recommendation against the use of remdesivir in hospitalized patients with COVID-19, regardless of disease severity. This guidance adds to recommendations published in the previous version: a strong recommendation for systemic corticosteroids in patients with severe and critical COVID-19, and a conditional recommendation against systemic corticosteroids in patients with non-severe COVID-19.

How this guideline was created: This living guideline is an innovation from WHO, driven by the urgent need for global collaboration to provide trustworthy and evolving COVID-19 guidance informing policy and practice world-wide. WHO has partnered with the non-profit Magic Evidence Ecosystem Foundation (MAGIC) for methodologic support and development and dissemination of living guidance for COVID-19 drug treatments, based on a living systematic review and network analysis (2). An international Guideline Development Group (GDG) of content experts, clinicians, patients, ethicists and methodologists produced recommendations following standards for trustworthy guideline development using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. No conflict of interest was identified for any panel member.

The latest evidence: The recommendation on remdesivir was informed by results from a systematic review and network meta-analysis (NMA) that pooled data from four randomized trials with 7333 participants hospitalized for COVID-19. The resulting GRADE evidence summary suggested that remdesivir has possibly no effect on mortality (odds ratio 0.90, 95% confidence interval [CI] 0.70 - 1.12; absolute effect estimate 10 fewer deaths per 1000 patients, 95% CI from 29 fewer - 11 more deaths per 1000 patients; low certainty evidence); and possibly no effect on the other important outcomes identified by the panel, with similar low to very low certainty of evidence. The panel judged the overall credibility of subgroup analyses assessing differences in mortality by severity of illness to be insufficient to make subgroup recommendations.

Understanding the recommendations: When moving from evidence to the conditional recommendation against the use of remdesivir in hospitalized patients with COVID-19, the panel emphasized the evidence suggesting no important effect on mortality, need for mechanical ventilation, time to clinical improvement, and other patient-important outcomes. Considering the low or very low certainty evidence for all outcomes, the panel concluded that the evidence did not prove that remdesivir has no benefit; rather, there is no evidence based on currently available data that it does improve patient-important outcomes. The panel placed low value on small and uncertain benefits in the presence of the remaining possibility of important harms. In addition, the panel considered contextual factors such as resources, feasibility, acceptability and equity for countries and health care systems.

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Info box

This living WHO guideline on therapeutics for COVID-19 now includes a conditional recommendation against the use of remdesivir, triggered by results from the WHO SOLIDARITY trial (1). Recommendations for hydroxychloroquine and lopinavir will follow. The first version of the living WHO guideline (published 2 September 2020) provides recommendations for corticosteroids.

This is a living guideline, so the recommendations included here will be updated, and new recommendations will be added on other therapies for COVID-19. The guideline is therefore written, disseminated and updated in MAGICapp, with a format and structure aiming to make it user-friendly and easy to navigate while accommodating for dynamically updated evidence and recommendations, focusing on what is new while keeping existing recommendations within the guideline.

Please visit the WHO website for the latest version of the guidance, also available in the BMJ as Rapid Recommendations together with the living NMA, a major evidence source for the guidelines (2).

At the time of publication (20 November 2020), the updated living NMA that informs this recommendation has been made available in the Annex (while undergoing peer review in the BMJ); and the systematic review and meta-analysis of adverse effects from remdesivir is also available as a preprint (3).

2. ABBREVIATIONS

ALT alanine aminotransferase ARDS acute respiratory distress syndrome CI confidence interval COVID-19 coronavirus disease 2019 eGFR estimated glomerular filtration rate GDG Guideline Development Group GRADE Grading of Recommendations Assessment, Development and Evaluation MAGIC Magic Evidence Ecosystem Foundation MD mean difference NMA network meta-analysis PICO population, intervention, comparator, outcome RCT randomized controlled trial SAE serious adverse event WHO World Health Organization

3. BACKGROUND

As of 11 November 2020, over 50 million people worldwide have been diagnosed with COVID-19, according to the WHO dashboard (4). The pandemic has so far claimed more than 1.2 million lives, and many areas of the world are experiencing a resurgence in cases. The COVID-19 pandemic – and the explosion of both research and misinformation – has highlighted the need for trustworthy, accessible and regularly updated (living) guidance to place emerging findings into context and provide clear recommendations for clinical practice.

This living guideline responds to emerging evidence from RCTs on existing and new drug treatments for COVID-19. More than 2800 trials on COVID-19 interventions have been registered or are ongoing (see section on emerging evidence) (5). Among these are large national and international platform trials (e.g. RECOVERY, WHO SOLIDARITY and DISCOVERY) that recruit very large numbers of patients in many countries, with a pragmatic and adaptive design (1, 6). These platform trials are currently investigating and reporting on drugs such as remdesivir, corticosteroids, hydroxychloroquine and lopinavir-ritonavir, with other interventions underway (e.g. convalescent plasma, immune-modulatory therapies). This rapidly evolving evidence landscape requires trustworthy interpretation and expeditious clinical practice guidelines to inform clinicians, patients, governments, ministries and health administrators.

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3.1 What triggered this version of the guideline?

This second version of the WHO living guideline addresses the use of remdesivir in patients with COVID-19. It follows the pre-print publication of the WHO SOLIDARITY trial on 15 October 2020, reporting results on treatment with remdesivir, hydroxychloroquine and lopinavir-ritonavir in hospitalized patients with COVID-19 (1). The role of these drugs in clinical practice has remained uncertain, with limited prior trial evidence. The WHO SOLIDARITY trial adds 11 266 randomized patients (2570 to remdesivir, 954 to hydroxychloroquine, 1411 to lopinavir-ritonavir, 6331 to usual care) and holds the potential to change practice (1).

The WHO GDG started with developing trustworthy recommendations on remdesivir, and plans recommendations on hydroxychloroquine and lopinavir-ritonavir to follow shortly. Remdesivir is a novel monophosphoramidate adenosine analogue prodrug which is metabolized to an active tri-phosphate form that inhibits viral RNA synthesis. Remdesivir has in vitro and in vivo antiviral activity against several viruses, including SARS-CoV-2. Remdesivir is widely used in many countries, with several guidelines recommending its use in patients with severe or critical COVID-19 (7, 8).

3.2 Who made this guideline?

As detailed in the Methods section, the WHO convened a standing GDG with 28 clinical content experts, 4 patient-partners and one ethicist, headed by a clinical chair (Dr Michael Jacobs) and methods chair (Dr Bram Rochwerg). WHO selected GDG members to ensure global geographical representation, gender balance, and appropriate technical and clinical expertise. No conflict of interest was identified for any panel member. MAGIC provided methodological experts with high-level expertise in standards and methods for systematic reviews and guideline development, including GRADE; in addition MAGIC offered innovations in processes (BMJ Rapid Recommendations) and platforms (MAGICapp) for developing living guidance in user-friendly formats. The methodological experts were not involved in the formulation of recommendations. MAGIC also worked with the BMJ to coordinate the simultaneous scientific publication of the living WHO guidelines (9).

3.3 How to use this guideline

This is a living guideline from the WHO. Recommendations will be updated, and new recommendations will be added on other therapies for COVID-19, including hydroxychloroquine and lopinavir-ritonavir (9). The guideline is written, disseminated and updated in MAGICapp, with a format and structure aiming to make it user-friendly and easy to navigate. It accommodates dynamic updating of evidence and recommendations that can focus on what is new while keeping existing recommendations, as appropriate, within the guideline. Section 4 outlines the key methodological aspects of the living guideline process.

The guideline is available via: the WHO website; online, multilayered formats (through MAGICapp), PDF-formats; and as BMJ Rapid Recommendations and WHO Academy app (9). The purpose of the online formats and additional tools, such as the infographics made by the BMJ, is to make it easier to navigate and make use of the guideline in busy clinical practice. The online, multilayered formats are designed to allow end users to find recommendations first and then drill down to find supporting evidence and other information pertinent to applying the recommendations in practice, including tools for shared decision-making (encounter decision aids).

4. METHODS: HOW THIS GUIDELINE WAS CREATED

The living WHO guideline is developed according to standards and methods for trustworthy guidelines, making use of an innovative process to achieve efficiency in dynamic updating of recommendations. The methods are aligned with the WHO Handbook for guideline development and according to a pre-approved protocol (planning proposal) by the WHO Guideline Review Committee (https://www.who.int/publications/guidelines/guidelines_review_committee/en/).

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Related guidelines This living WHO guidance for COVID-19 treatments will be related to the larger, more comprehensive guidance for Clinical management of COVID-19: interim guidance, which has a wider scope of content (10). The first WHO living guidance published, Corticosteroids for COVID-19: living guidance, was well received by stakeholders and disseminated via the BMJ and MAGICapp.

Timing This guidance aims to be trustworthy and living; dynamically updated and globally disseminated once new evidence warrants a change in recommendations for COVID-19 therapeutics. We aim for an ambitious timeframe from trials that trigger the guideline development process to WHO publication, within 1 month, while maintaining standards and methods for trustworthy guidelines (WHO Handbook of guideline development).

Here we outline the stepwise approach we take to improve efficiency and timeliness of the living, trustworthy guidance, in the development and dissemination of the recommendations. To do so, various processes occurred simultaneously.

Stepwise approach Step 1: Evidence monitoring and mapping and triggering of evidence synthesis Comprehensive daily monitoring of all emerging RCTs occurs on a continuous basis, within the context of the living systematic review and NMA, using experienced information specialists, who look at all relevant information sources for new RCTs addressing interventions for COVID-19. Once practice-changing evidence is identified, such as in this case the SOLIDARITY trial pre-print, the WHO Therapeutics Steering Committee triggered the guideline development process, with the Guidance Support Collaboration Committee (see acknowledgements), PICO development and construction of evidence summaries addressing the intervention of interest initiated.

The trigger for producing or updating specific recommendations is based on the following: • likelihood to change practice;• sufficient RCT data on therapeutics to inform the high-quality evidence synthesis living systematic

review; and• relevance to a global audience.

Step 2: Convening the GDG The pre-selected expert panel (see Acknowledgments) convened on five occasions over a 2-week period. The first meeting, held 13 October 2020, reviewed the basics of GRADE methodology; including formulating PICO questions and subgroups of interests, assessment of certainty of evidence, incorporating patients’ values and preferences, and prioritization of patient-important outcomes. The second meeting, held on 20 October 2020, finalized the outcome prioritization, PICOs and pre-specified subgroups for this specific question. At the third meeting, held on 23 October 2020, a Q&A session was held with the individual study investigators and biostatisticians: SOLIDARITY (Drs Ana Maria Henao Restrepo and Richard Peto); ACTT-1 (Drs Lori Dodd and John Beigel); and RECOVERY (Drs Peter Horby and Jonathan Emberson). At the fourth meeting, held on 27 October 2020, evidence summaries were shown to the GDG panel, including pre-specified subgroup analysis and preliminary recommendations were drafted. An additional fifth meeting was convened on 5 November 2020, to review a post hoc subgroup analysis and allow for final deliberation on values and preference statements, as well as recommendations.

Step 3: Evidence synthesis The living systematic review/NMA team, as requested by the WHO Therapeutics Steering Committee and coordinated by the Guidance Support Collaboration Committee, was ready to perform an independent systematic review to examine the benefits and harms of the intervention. The systematic review team is multidisciplinary and made up of systematic review experts, clinical experts, clinical epidemiologists, graduate students and biostatisticians. The team has expertise in GRADE methodology and rating certainty of evidence specifically in NMAs. The NMA team was informed by the deliberations from the initial two GDG meetings in order to guide the NMA, specifically focusing on the outcomes and subgroups prioritized by the panel. After the fourth meeting, the NMA team continued with a post hoc subgroup analysis using a Bayesian approach and meta-regression. This was shown to the panel at the last panel meeting.

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Step 4: Final recommendations The GRADE approach provided the framework for establishing evidence certainty and generating both the direction and strength of recommendations. Although a priori voting procedures were established at the outset, in case consensus was not reached, these procedures were not necessary for this recommendation, which reached consensus amongst the panel.

The following key factors were used to formulate transparent and trustworthy recommendations: • absolute benefits and harms for all patient-important outcomes through structured evidence summaries

(e.g. GRADE summary of findings tables);• quality/certainty of the evidence;• values and preferences of patients;• resources and other considerations (including considerations of feasibility, applicability, equity);• each outcome will have an effect estimate and confidence interval, with a measure of certainty in the

evidence, as presented in summary of findings tables. If such data are not available narrative summarieswill be provided;

• recommendations will be rated as either conditional or strong, as defined by GRADE. If the panelmembers disagree regarding the evidence assessment or strength of recommendations, WHO will applyvoting according to established methods.

Step 5: External and internal review: The WHO guideline was then reviewed by pre-specified external reviewers (see Acknowledgements) and then approved by the WHO Publication Review Committee.

5. THE EVIDENCE

This section outlines what information the GDG panel requested and used in making their recommendation for remdesivir.

Benefits and harms: relative effects of remdesivir on patient-important outcomes The GDG panel requested an update of the living NMA of RCTs of drug treatments for COVID-19, based around important clinical questions to be addressed in the recommendations. The GDG members prioritized outcomes (rating from 1 [not important] to 9 [critical]), taking a patient's perspective (Table 1). The panel’s questions were structured using the PICO format (see evidence profile under the recommendations).

Table 1. Panel outcome rating from a patient perspective

Note: 1: not important, 9: critically important.

Based on 4 trials with 7333 participants (1, 11-13), the NMA provided relative estimates of effect for patient-important outcomes (Table 2). Of note, none of the included studies enrolled children or adolescents under the age of 19 years old.

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Table 2. Summary of trials and trial characteristics informing the remdesivir recommendation

Study N Country Mean age (years)

Severity (as per WHO criteria)

% IMV (at baseline)

Treatments (dose and duration) Outcomes

Biegel (ACTT-1) 1063

United States, Europe,

Asia

58.9 Non-severe (11.3%)

Severea (88.7%) 44.1%

Remdesivir IV (100 mg/day for 10

days)

-Mortality-Adverse events-Time to clinical

improvement

Spinner (SIMPLE

MODERATE)* 596

United States, Europe,

Asia

56-58 Non-severe (100%) 0%

Remdesivir IV (200 mg at day 1, then 100 mg for 4 days or 9

days)

-Mortality-Time to clinical

improvement-Duration of

hospitalization-Mechanical ventilation

-Adverse events

Pan (SOLIDARITY) 5451 Worldwide

< 50 35% 50-70 47%> 70 18%

Non-severe (24%) Severeb (67%) Critical (9%)

8.9% Remdesivir IV

(200 mg at day 1, then 100 mg day 2-10)

-Mortality-Mechanical ventilation

Wang 237 China 65 Severec (100%) 16.1% Remdesivir IV

(100 mg/day for 10 days)

-Mortality-Mechanical ventilation

-Adverse events-Viral clearance

-Duration ofhospitalization

-Duration of ventilation-Time to clinical

improvementNotes: IMV – invasive mechanical ventilation; IV – intravenous; N – number; NR (not reported); Sx – symptom. Severity criteria based on WHO definitions unless otherwise stated: a defined severe as SpO2 < 94% on room air OR respiratory rate > 24 breaths /min; b defined severe as requiring oxygen support; c defined severe as SpO2 < 94% on room air *Only SIMPLE MODERATE was included in the analysis, as SIMPLE SEVERE (14) did not have a placebo/usual care arm.

Subgroup analysis The GDG panel requested subgroup analyses based on age (considering children vs adults vs older people), illness severity (non-severe vs severe vs critical COVID-19 – see subgroup analysis under 7.1 Recommendations for details), and duration of remdesivir therapy (5 days vs longer than 5 days). The GDG discussed other potential subgroups of interest including time from onset of symptoms until initiation of therapy, and concomitant medications (especially corticosteroids), however recognized these analyses would not be possible without access to individual participant data. To this last point, the panel recognized that usual care is likely variable between centres, regions and evolved over time. However, given all of the data comes from RCTs, use of these co-interventions that comprise usual care should be balanced between study patients randomized to either the intervention or usual care arms.

Following the panel's request, the NMA team performed subgroup analyses in order to assess for effect modification which, if present, could mandate distinct recommendations by subgroups. From the data available from the included trials, subgroup analysis was only possible for severity of illness and the outcome of mortality. This subgroup analysis was performed using a random effects frequentist analysis based on the three WHO severity definitions. A post hoc Bayesian analysis was also performed, which incorporated meta-regression using study as a random effect. This latter approach has the advantage of more accurately accounting for within-study differences but can only compare two subgroups at a time. The panel used a pre-specified framework incorporating the ICEMAN tool to assess the credibility of subgroup findings (15).

Baseline risk estimates (prognosis of patients with COVID-19): informing absolute estimates of effect The evidence summaries that informed the guideline recommendation reported the anticipated absolute effects of remdesivir compared with usual care across all patient-important outcomes, with explicit judgments of certainty in the evidence for each outcome. The absolute effects of treatment are informed by the prognosis (i.e. baseline risk estimates) combined with the relative estimates of effects (e.g. risk ratios, odds ratio) obtained from the NMA.

The control arm of the WHO SOLIDARITY trial (1), performed across a wide variety of countries and geographical regions, was identified by the GDG panel as representing the most relevant source of evidence to make the baseline risk estimates for the outcomes of mortality and mechanical ventilation. The rationale for selecting the WHO

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SOLIDARITY trial was to reflect the overall prognosis of the global population for which the WHO guideline recommendations are made. In view of the study designs, the GDG determined that for other outcomes using the median or mean of all patients randomized to usual care across the included studies would provide the most reliable estimate of baseline risk.

Values and preferences There were insufficient published data to provide the GDG with an informative systematic review of studies describing patients' experiences or values and preferences on treatment decisions for COVID-19 drug treatments. The GDG therefore relied on their own judgments of what well-informed patients would value after carefully balancing the benefits, harms and burdens of treatment and their subsequent treatment preferences. The GDG included four patient-representatives who had lived experience with COVID-19.

The GDG agreed that the following values and preferences would be representative of those of typical well-informed patients:

• Mortality would be the outcome most important to patients, followed by need and duration ofmechanical ventilation, time to clinical improvement, and serious intervention-related adverse events.

• Most patients would be reluctant to use a medication for which the evidence left high uncertaintyregarding effects on the outcomes listed above. This was particularly so when evidence suggestedtreatment effects, if they do exist, are small, and the possibility of important harm remains.

• In an alternative situation with larger benefits and less uncertainty regarding both benefits and harms,more patients would be inclined to choose the intervention.

The GDG acknowledged, however, that values and preferences are likely to vary. There will be patients inclined to use a treatment in which evidence has not excluded important benefit, particularly when the underlying condition is potentially fatal. On the other hand, there will be those who have a high threshold of likely benefit before they will choose the intervention. Although the GDG focused on an individual patient perspective, they also considered a population perspective in which feasibility, acceptability, equity and cost are important considerations.

6. WHO DO THE RECOMMENDATIONS APPLY TO?The guideline for COVID-19 therapeutics applies to hospitalized patients with COVID-19. For some drugs (such as corticosteroids), recommendations may differ based on the severity of COVID-19 disease. The GDG elected to use the WHO severity definitions based on clinical indicators, adapted from WHO COVID-19 disease severity categorization (see below) (10). These definitions avoid reliance on access to health care to define patient subgroups. The infographic illustrates these three disease severity groups and key characteristics to apply in practice.

Critical COVID-19: Defined by the criteria for acute respiratory distress syndrome (ARDS), sepsis, septic shock, or other conditions that would normally require the provision of life-sustaining therapies such as mechanical ventilation (invasive or non-invasive) or vasopressor therapy.

Severe COVID-19: Defined by any of: • oxygen saturation < 90% on room air;• respiratory rate > 30 breaths/min in adults and children > 5 years old; ≥ 60 breaths/min in children < 2

months old;≥ 50 in children 2–11 months old; and ≥ 40 in children 1–5 years old;

• signs of severe respiratory distress (accessory muscle use, inability to complete full sentences, and, inchildren, very severe chest wall indrawing, grunting, central cyanosis, or presence of any other generaldanger signs).

Non-severe COVID-19: Defined as absence of any criteria for severe or critical COVID-19.

Caution: The panel noted that the oxygen saturation threshold of 90% to define severe COVID-19 was arbitrary and should be interpreted cautiously when used for determining which patients should be offered systemic corticosteroids. For example, clinicians must use their judgment to determine whether a low oxygen

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saturation is a sign of severity or is normal for a given patient with chronic lung disease. Similarly, a saturation > 90–94% on room air is abnormal (in patient with normal lungs) and can be an early sign of severe disease, ifthe patient is on a downward trend. Generally, if there is any doubt, the panel suggested erring on the side ofconsidering the illness as severe.

Reproduced from the BMJ Rapid Recommendations (copyright managed by MAGIC).

7. RECOMMENDATIONS FOR THERAPEUTICS

7.1 Remdesivir

Hospitalized patients with COVID-19, regardless of disease severity

Conditional recommendation

We suggest against administering remdesivir in addition to standard care.

Evidence to decision

Benefits and harms Possibly no benefit, or little difference, compared with usual care alone

The GDG panel found a lack of evidence that remdesivir improved outcomes that matter to patients such as reduced mortality, need for mechanical ventilation, time to clinical improvement and others. However, the low certainty evidence for these outcomes, especially mortality, does not prove that remdesivir is ineffective; rather, there is insufficient evidence to confirm that it does improve patient-important outcomes.

There was no evidence of increased risk of severe adverse events (SAEs) from the trials. However, further pharmacovigilance is needed because SAEs are commonly underreported and rare events could be missed, even in large RCTs.

A subgroup analysis indicated that remdesivir treatment possibly increased mortality in the critically ill and possibly reduced mortality in the non-severely and severely ill. The panel judged the overall credibility of this subgroup effect (evaluated using the ICEMAN tool) to be insufficient to make subgroup recommendations. The overall low certainty evidence on the benefits and harms of remdesivir, driven by risk of bias and imprecision limitations in the included studies, also contributed to the judgment.

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Certainty of the evidence Low

The evidence is based on a linked systematic review and network meta-analysis of 4 RCTs; pooling data from 7333 patients hospitalized with various severities of COVID-19 and variably reporting the outcomes of interest to the guideline panel. The panel agreed that there was low certainty in the estimates of effect for all patient-important outcomes across benefits and harms, mostly driven by risk of bias and imprecision (wide confidence intervals which don't exclude important benefit or harm). There was very low certainty evidence for viral clearance and delirium.

Preference and values Substantial variability is expected or uncertain

Applying the agreed values and preferences (see Evidence section), the GDG inferred that most patients would be reluctant to use remdesivir given the evidence left high uncertainty regarding effects on mortality and the other prioritized outcomes. This was particularly so as any beneficial effects of remdesivir, if they do exist, are likely to be small and the possibility of important harm remains. The panel acknowledged, however, that values and preferences are likely to vary, and there will be patients and clinicians who choose to use remdesivir given the evidence has not excluded the possibility of benefit.

Resources implications, feasibility, equity and other considerations

Important issues, or potential issues not investigated

A novel therapy typically requires higher certainty evidence of important benefits than currently available for remdesivir, preferably supported wherever possible by cost-effectiveness analysis. In the absence of this information, the GDG raised concerns about opportunity costs and the importance of not drawing attention and resources away from best supportive care or the use of corticosteroids in severe COVID-19. It was noted that remdesivir is administered only by the intravenous route currently, and that global availability is currently limited.

Justification When moving from evidence to the conditional recommendation against the use of remdesivir for patients with COVID-19, the panel emphasized the evidence of possibly no effect on mortality, need for mechanical ventilation, recovery from symptoms and other patient-important outcomes, albeit of low certainty; it also noted the anticipated variability in patient values and preferences, and other contextual factors, such as resource-considerations, accessibility, feasibility and impact on health equity (see summary of these factors under Evidence to decision).

Importantly, given the low certainty evidence for these outcomes, the panel concluded that the evidence did not prove that remdesivir has no benefit; rather, there is no evidence based on currently available data that it does improve patient-important outcomes. Especially given the costs and resource implications associated with remdesivir, but consistent with the approach that should be taken with any new drug, the panel felt the responsibility should be on demonstrating evidence of efficacy, which is not established by the currently available data. The panel noted that there was no evidence of increased risk of SAEs in patients receiving remdesivir, at least from the included trials. Further pharmacovigilance is required to confirm this, as SAEs are commonly underreported and rare events would be missed, even in large RCTs.

Subgroup analysis The panel carefully considered a potential subgroup effect across patients with different levels of disease severity, suggesting a possible increase in mortality in the critically ill and a possible reduction in mortality in the non-severely and severely ill. For this analysis, critical illness was defined as those requiring invasive or non-invasive ventilation; severe illness as those requiring oxygen therapy (but not meeting critical illness criteria); and non-severe as all others. Patients requiring high-flow nasal cannula represented a small proportion and were characterized as either severe (SOLIDARITY) or critical (ACTT-1, Wang). The analysis focused on within-study subgroup comparisons across the different severities, and therefore the SIMPLE-MODERATE trial could not be included in the subgroup analysis as it only enrolled patients with non-severe COVID-19. The panel reviewed the results of both the random effects frequentist analysis and the post hoc Bayesian analysis which incorporated meta-regression using study as a random effect.

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The GDG panel judged the credibility in the subgroup analysis assessing differences in mortality by severity of illness to be insufficient to make subgroup recommendations. Important factors influencing this decision included a lack of a priori hypothesized direction of subgroup effect by trial investigators, little or no previously existing supportive evidence for the subgroup finding, and relatively arbitrary cut points used to examine the subgroups of interest. The overall low certainty evidence for the benefits and harms of remdesivir, driven by risk of bias and imprecision limitations, also contributed to the judgment. The panel highlighted that despite the conditional recommendation against remdesivir, they support further enrolment into RCTs evaluating remdesivir, especially to provide higher certainty of evidence for specific subgroups of patients.

The panel had a priori requested analyses of other important subgroups of patients including children and older persons, but there were no data to address these groups specifically. None of the included RCTs enrolled children, and although older people were included in the trials, their outcomes were not reported separately. Also, there are no pharmacokinetic or safety data on remdesivir for children. Given this, the applicability of this recommendation to children is currently uncertain.

Practical information The GDG made a conditional recommendation against using remdesivir for treatment of hospitalized patients with COVID-19. If administration of remdesivir is considered, it should be noted that its use is contraindicated in those with liver (ALT > 5 times normal at baseline) or renal (eGFR < 30 mL/minute) dysfunction. To date, it can only be administered intravenously, and it has relatively limited availability.

PICO Population: patients with COVID-19 (all disease severities) Intervention: remdesivir + usual care Comparator: usual care

Outcome Timeframe

Study results and measurements

Absolute effect estimates Certainty of the evidence

(Quality of evidence) Plain text summary

Standard care Remdesivir

Mortality Odds ratio: 0.90

(95% CI 0.70 - 1.12) Based on data from 7333

patients in 4 studies

106 per 1000

96 per 1000 Low

Due to serious risk of bias and serious imprecision

Remdesivir possibly has no effect on

mortality. Difference: 10 fewer per 1000 (95% CI 29 fewer - 11 more)

Mechanical ventilation

Odds ratio: 0.89 (95% CI 0.76 - 1.03)

Based on data from 6549 patients in 4 studies

105 per 1000

95 per 1000 Low

Due to serious risk of bias and serious imprecision


mechanical ventilation. Difference: 10 fewer per 1000 (95% CI 23 fewer - 3 more)

Serious adverse events leading to discontinuation

Odds ratio: 1.00 (95% CI 0.37 - 3.83)


15 per 1000

15 per 1000

Low Due to very serious imprecision


serious adverse events leading to

discontinuation. Difference: 0 fewer per 1000

(95% CI 9 fewer - 40 more)

Viral clearance 7 days

Odds ratio: 1.06 (95% CI 0.06 - 17.56)

Based on data from 196 patients in 1 study

483 per 1000

498 per 1000

Very Low Due to very serious imprecision

The effect of remdesivir on viral

clearance is uncertain. Difference: 15 more per 1000 (95% CI 430 fewer - 460 more)

Acute kidney injury

Odds ratio: 0.85 (95% CI 0.51 - 1.41)


56 per 1000

48 per 1000 Low

Due to serious imprecision and serious indirectness

Remdesivir possibly has no effect on acute

kidney injury. Difference: 8 fewer per 1000 (95% CI 27 fewer - 21 more)

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Delirium Odds ratio: 1.22

(95% CI 0.48 - 3.11) Based on data from 1048

patients in 1 study

16 per 1000

19 per 1000 Very low

Due to very serious imprecision and serious indirectness

The effect of remdesivir on delirium

is uncertain. Difference: 3 more per 1000 (95% CI 8 fewer - 32 more)

Time to clinical improvement

Measured by: Scale: lower better


11.0 Days mean

9.0 Days mean Low


Remdesivir possibly has no effect on time

to clinical improvement. Difference: MD 2.0 lower

(95% CI 4.2 lower - 0.9 higher)

Duration of hospitalization



12.8 Days mean

12.3 Days mean Low



duration of hospitalization. Difference: MD 0.5 lower

(95% CI 3.3 lower - 2.3 higher)

Duration of ventilation



14.7 Days mean

13.4 Days mean

Low Due to very serious imprecision


duration of ventilation. Difference: MD 1.3 lower (95% CI 4.1 lower - 1.5 higher)

Source: Siemieniuk et al., 2020 (2).

8. UNCERTAINTIES, EMERGING EVIDENCE AND FUTURE RESEARCH

The guideline recommendations for COVID-19 therapeutics demonstrate remaining uncertainties concerning treatment effects for all outcomes of importance to patients. There is also a need for better evidence on prognosis and values and preferences of patients with COVID-19. Here we outline key uncertainties for remdesivir identified by the GDG, adding to those for corticosteroids in the first version of the living guideline. These uncertainties may inform future research, i.e. the production of more relevant and reliable evidence to inform policy and practice. We also outline emerging evidence in the rapidly changing landscape of trials for COVID-19.

Ongoing uncertainties and opportunities for future research

Remdesivir and its effects on: • critical outcomes of interest, particularly those that impact resource allocation, such as the need for

mechanical ventilation, duration of mechanical ventilation and duration of hospitalization;• specific subgroups, such as different severities of illness, different time (days) since onset of illness,

children and older adults, pregnant women, and duration of therapy;• long-term outcomes such as mortality at extended endpoints or long-term quality of life;• long-term safety and rare but important side-effects;• patient-reported outcomes such as symptom burden;• outcomes, when used in combination with other agents, such as, but not limited to, corticosteroids;• impact on viral shedding, viral clearance, patient infectivity.

Emerging evidence The unprecedented volume of planned and ongoing studies for COVID-19 interventions – 2801 RCTs as of 1 November 2020 – implies that more reliable and relevant evidence will emerge to inform policy and practice (5). An overview of registered and ongoing trials for COVID-19 therapeutics is available from the Infectious Diseases Data Observatory, through their living systematic review of COVID-19 clinical trial registrations (5) and the WHO website (https://www.covid-nma.com/dataviz/).

Whereas most of these studies are small and of variable methodological quality, a number of large, international platform trials (e.g. RECOVERY, SOLIDARITY and DISCOVERY) are better equipped to provide robust evidence for a number of potential treatment options (1). Such trials can also adapt their design, recruitment strategies and selection of interventions based on new insights, exemplified by the uncertainties outlined above.

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For remdesivir 36 trials have been registered and 6 are completed. The median (25th, 75th percentile) planned sample size of these trials is 260 (80, 1062) (Figure 1). Further details of all registered trials are in Supplementary Table 1 in the WHO living guideline on COVID-19 therapeutics published in the BMJ (9).

Figure 1. Sample size of remdesivir randomized controlled trials

AUTHORSHIP, CONTRIBUTIONS, ACKNOWLEDGEMENTS

This guidance was developed in collaboration with the non-profit Magic Evidence Ecosystem Foundation (MAGIC), which provided methodologic support to develop and disseminate living guidance for COVID-19 drug treatments. The WHO would like to thank the collaborative efforts of all those involved to make this process rapid, efficient, trustworthy and transparent, including extensive in-kind support from MAGIC and their partner the BMJ to develop and disseminate this living guidance for COVID-19 drug treatments, based on a living systematic review and network analysis from investigators at McMaster University, Canada.

WHO Therapeutics Steering Committee The committee includes representatives from various WHO departments at headquarters and the regions and has been approved by the WHO Director of the Country Readiness Department and the WHO Chief Scientist. The WHO Secretariat meets on a regular basis to discuss when to trigger guideline updates based on evidence updates from the WHO rapid review team, and other sources of evidence and selects the members of the Guideline Development Group (GDG) for living guidance.

Janet V Diaz (Lead, Clinical Team for COVID-19 Response, Health Emergencies Programme, Geneva); John Appiah (Lead, case management, WHO Regional Office for Africa); Lisa Askie (Quality Assurance of Norms and Standards Department); Silvia Bertagnolio (Communicable and Noncommunicable Diseases Division/Clinical Team for COVID-19 Response); Nedret Emiroglu (Country Readiness Strengthening, Health Emergencies Department); John Grove (Quality Assurance of Norms and Standards Department); Rok Ho Kim (Quality Assurance of Norms and Standards Department); Chiori Kodama (Regional Office for the Eastern Mediterranean/Health Emergencies Programme); Gary Kuniyoshi (WHO Regional Office for the Western Pacific/Health Emergencies Programme); Lorenzo Moja (Health Products Policy and Standards Department); Olufemi Oladapo (Sexual and Reproductive Health and Research Department); Dina Pfeifer (WHO Regional Office for Europe/Health Emergencies Programme); Jacobus Preller (Clinical Team for COVID-19 Response); Pryanka Relan (Integrated Health Services Department/Clinical Team for COVID-19 Response); Ludovic Reveiz (Evidence and Intelligence for Action in Health Department, Incident Management Systems for COVID-19, Pan American Health Organization); Soumya Swaminathan (Office of Chief Scientist);Wilson Were (Maternal, Newborn, Child and Adolescent Health and Ageing Department); Pushpa Wijesinghe (Lead, case management, Regional Office for South-East Asia). Supporting project officer: Jacqueline Lee Endt (Health Care Readiness Unit, Health Emergencies Department).

The WHO Therapeutics Steering Committee is fully responsible for decisions about guidance production and convening the GDG.

We would like to acknowledge the Guidance Support Collaboration Committee which provided the coordination between WHO and MAGIC to allow the rapid development of the WHO guidance and its dissemination: Thomas Agoritsas (MAGIC, University Hospitals, Geneva); Janet Diaz (WHO), Fiona Godlee (British Medical Journal); Gordon Guyatt (McMaster University, Canada); Navjoyt Ladher (British Medical Journal); François Lamontagne (Université de Sherbrooke, Canada); Srinivas Murthy (University of British Colombia, Canada); Bram Rochwerg (McMaster University, Canada), Per Olav Vandvik (MAGIC, University of Oslo).

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Guideline Development Group (GDG) Wagdy Amin (Ministry of Health and Population, Egypt); Frederique Bausch (Geneva University Hospital, Switzerland); Erlina Burhan (Infection Division Department of Pulmonology and Respiratory Medicine, Faculty of Medicine, Universitas Indonesia); Maurizio Cecconi (Humanitas Research Hospital Milan, Italy) Duncan Chanda (Adult Infectious Disease Centre, University Teaching Hospital, Lusaka, Zambia); Vu Quoc Dat (Department of Infectious Diseases, Hanoi Medical University, Viet Nam); Bin Du (Peking Union Medical College Hospital); Heike Geduld (Emergency Medicine, Stellenbosch University, South Africa); Patrick Gee (patient panel member, United States of America); Madiha Hashimi (Ziauddin University Karachi, Pakistan); Manai Hela (Emergency Medical Service Tunis, Tunisia); Sushil Kumar Kabra (All India Institute of Medical Sciences, New Delhi, India); Seema Kanda (patient panel member, Ontario, Canada); Leticia Kawano-Dourado (Research Institute, Hospital do Coração, São Paulo, Brazil); Yae-Jean Kim (Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Republic of Korea); Niranjan Kissoon (Department of Paediatrics and Emergency Medicine, University of British Columbia, Vancouver, Canada); Arthur Kwizera (Makerere College of Health Sciences, Kampala, Uganda); Imelda Mahaka (patient panel member, Pangaea Harare, Zimbabwe); Greta Mino (Alcivar Hospital, Guayaquil, Ecuador); Emmanuel Nsutebu (Sheikh Shakhbout Medical City, Abu Dhabi); Natalia Pshenichnaya (Central Research Institute of Epidemiology of Rospotrebnadzor, Moscow, Russia); Nida Qadir (Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California, Los Angeles, USA); Saniya Sabzwari (Aga Khan University Karachi, Pakistan); Rohit Sarin (National Institute of Tuberculosis and Respiratory Diseases, New Delhi, India); Michael Sharland (St George’s University Hospitals NHS Foundation Trust, London); Yinzhong Shen (Shanghai Public Health Clinical Center, Fudan University, Shanghai, China); Joao Paulo Souza (University of São Paulo Brazil); Shalini Sri Ranganathan (University of Colombo, Sri Lanka); Sebastian Ugarte (Faculty of Medicine Andres Bello University, Indisa Clinic, Santiago, Chile); Sridhar Venkatapuam (King’s College, London); Dubula Vuyiseka (patient panel member, University of Stellenbosch, South Africa); Ananda Wuewickrama (Ministry of Health Sri Lanka).

Methods Chair: Bram Rochwerg (McMaster University).

Clinical Chair: Michael Jacobs (Royal Free London NHS Foundation Trust).

Methods resource persons: Arnav Agarwal (University of Toronto, Canada); Thomas Agoritsas (University Hospitals, Geneva, Switzerland); Romina Brignardello-Peterson (McMaster University, Canada); Gordon Guyatt (McMaster University, Canada); George Tomlinson (University Health Network Toronto, Canada); Per Olav Vandvik (MAGIC, University of Oslo Norway), Linan Zeng (West China Second University Hospital, Sichuan University, Chengdu, China; McMaster University, Canada).

NMA team: Arnav Agarwal, Thomas Agoritsas, Jessica J Bartoszko, Romina Brignardello-Petersen, Derek K Chu, Rachel Couban, Andrea Darzi, Tahira Devji, Bo Fang, Carmen Fang, Signe Agnes Flottorp, Farid Foroutan, Long Ge, Gordon H Guyatt, Mi Ah Han, Diane Heels-Ansdell, Kimia Honarmand, Liangying Hou, Xiaorong Hou, Quazi Ibrahim, Ariel Izcovich, Elena Kum, Francois Lamontagne, Qin Liu, Mark Loeb, Maura Marcucci, Shelley L McLeod, Sharhzad Motaghi, Srinivas Murthy, Reem A Mustafa, John D Neary, Hector Pardo-Hernandez, Anila Qasim, Gabriel Rada, Irbaz Bin Riaz, Bram Rochwerg, Behnam Sadeghirad, Nigar Sekercioglu, Lulu Sheng, Reed AC Siemieniuk, Ashwini Sreekanta, Charlotte Switzer, Britta Tendal, Lehana Thabane, George Tomlinson, Tari Turner, Per O Vandvik, Robin WM Vernooij, Andrés Viteri-García, Ying Wang, Liang Yao, Zhikang Ye, Dena Zeraatkar.

Special thanks to the study investigators for joining the Q&A session and providing additional data as requested to strengthen the analysis: ACTT-1: John Biegel; Lori Dodd (National Institute of Allergy and Infectious Diseases, Bethesda). SOLIDARITY: Sir Richard Peto (Nuffield Department of Population Health, University of Oxford); Ana Maria Henao Restrepo (Health Emergencies Preparedness and Response, WHO). RECOVERY: Jonathan Emberson; Peter Horby (University of Oxford).

Special thanks to the external reviewers for their insights: Yaseen Arabi (King Saud Bin Abdulaziz University for Health Sciences, Saudi Arabia); Richard Kojan (The Alliance for International Medical Action); Bhargavir Rao (Médecins Sans Frontières).

Main editors: Dr Michael Jacobs and Dr Bram Rochwerg (GDG co-chairs).

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REFERENCES

1. WHO SOLIDARITY Trial Consortium, Pan H, Peto R, Karim Q, Alejandria M, Henao-Restrepo AM et al. Repurposedantiviral drugs for COVID-19 – interim WHO SOLIDARITY trial results. MedRxiv 2020.

2. Siemieniuk RA, Bartoszko JJ, Ge L, Zeraatkar D, Izcovich A, Pardo-Hernandez H et al. Drug treatments for covid-19:living systematic review and network meta-analysis. BMJ. 2020;370 m2980.

3. Izcovich A, Siemieniuk RA, Bartoszko JJ, L Ge, Zeraatkar D, Kum E et al. Adverse effects of remdesivir,hydroxychloroquine, and lopinavir/ritonavir when used for COVID-19: systematic review and meta-analysis ofrandomized trials. [In peer review.] BMJ Open. 20 November 2020. Preprint available at:https://www.medrxiv.org/content/10.1101/2020.11.16.20232876v1

4. WHO. WHO coronavirus disease (COVID-19) dashboard. Geneva: World Health Organization; 2020.5. Maguire BJ, Guérin PJ. A living systematic review protocol for COVID-19 clinical trial registrations [version 1; peer

review: 2 approved]. Wellcome Open Res. 2020;5(60).6. RECOVERY Collaborative Group, Horby P, Lim WS, Emberson JR, Mafham M et al. Dexamethasone in hospitalized

patients with Covid-19 - Preliminary report. NEJM. 2020. DOI: 10.1056/NEJMoa2021436.7. Rochwerg B, Agarwal A, Zeng L, Leo Y-S, Appiah JA, Agoritsas T et al. Remdesivir for severe covid-19: a clinical practice

guideline. BMJ. 2020;370 m2924.8. COVID-19 Treatment Guidelines Panel. Coronavirus disease 2019 (COVID-19) treatment guidelines. Bethesda (MD):

National Institutes of Health; 2020.9. Lamontagne F, Agoritsas T, Macdonald H, Leo Y-S, Diaz J, Agarwal A et al. A living WHO guideline on drugs for covid-19.

BMJ. 2020;370 m3379.10. WHO. Clinical management of COVID-19: interim guidance. Geneva: World Health Organization: 2020.11. Beigel JH, Tomashek LE, Dodd, Mehta AK, Zingman BS, Kalil AC et al. Remdesivir for the treatment of Covid-19 – Final

report. NEJM. 2020;383:1813-1826. DOI: 10.1056/NEJMoa2007764.12. Spinner CD, Gottlieb RL, Criner GJ, Arribas López JR, Cattelan AM, Viladomiu AS et al. Effect of remdesivir vs standard

care on clinical status at 11 days in patients with moderate COVID-19. JAMA. 2020;324(11):1048-1057.13. Wang Y, Zhang D, Du G, Du R, Zhao J, Jin Y et al. Remdesivir in adults with severe COVID-19: a randomised double-

blind, placebo-controlled, multicentre trial. Lancet 2020;395:1569-78.14. Goldman JD, Lye DCB, Hui DS, Marks KM, Bruno R, Montejano R. Remdesivir for 5 or 10 days in patients with severe

Covid-19. NEJM, 2020;383:1827-37.15. Schandelmaier S, Briel M, Varadhan R, Schmid CH, Devasenapathy N, Hayward RA et al. Development of the Instrument

to assess the Credibility of Effect Modification Analyses (ICEMAN) in randomized controlled trials and meta-analyses.CMAJ. 2020;192(32):E901-E906.

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Drug treatments for covid-19: living systematic review and network meta-analysis version 3

Reed AC Siemieniuk, methodologist, internist,1 * Jessica J Bartoszko, methodologist,1 * Long Ge,

methodologist,2 * Dena Zeraatkar, methodologist,1 * Ariel Izcovich, methodologist, internist,3

Elena Kum, methodologist,1 Hector Pardo-Hernandez, methodologist,4,5 Bram Rochwerg,

methodologist, critical care physician,1,6 Francois Lamontagne, methodologist, critical care

physician,7 Mi Ah Han, methodologist,8 Qin Liu, professor,9,10 Arnav Agarwal, methodologist,

internist,1,11 Thomas Agoritsas, methodologist, internist,1,12 Derek K Chu, methodologist,

immunologist,1,6 Rachel Couban, librarian,13 Andrea Darzi, methodologist,1 Tahira Devji,

methodologist,1 Bo Fang, methodologist,9,10 Carmen Fang, registered nurse,14 Signe Agnes

Flottorp, senior researcher,15,16 Farid Foroutan, methodologist,1,17 Maryam Ghadimi, PhD

student, 1 Diane Heels-Ansdell, statistician,1 Kimia Honarmand, methodologist, critical care

physician,18 Liangying Hou, medical doctor candidate,2 Xiaorong Hou, librarian,19 Quazi

Ibrahim, statistician,1 Assem Khamis, data analyst,20 Bonnie Lam, student, 1 Mark Loeb,

methodologist, infectious disease physician,1,6 Maura Marcucci, methodologist, internist,1,6

Shelley L McLeod, methodologist, assistant professor,21,22 Sharhzad Motaghi, methodologist,1

Srinivas Murthy, clinical associate professor, pediatric critical care, infectious diseases

physician,23 Reem A Mustafa, associate professor, nephrologist,1,24 John D Neary,

methodologist, internist,6 Anila Qasim, research associate,1 Gabriel Rada, methodologist,25,26

Irbaz Bin Riaz, methodologist, internist,27 Behnam Sadeghirad, assistant professor,1,13 Nigar

Sekercioglu, assistant professor,1 Lulu Sheng, methodologist,9,10 Ashwini Sreekanta,

methodologist,1 Charlotte Switzer, methodologist,1 Britta Tendal, methodologist,28 Lehana

Thabane, professor,1 George Tomlinson, senior biostatistician,29 Tari Turner, senior research

fellow,28 Per O Vandvik, methodologist, internist,15 Robin WM Vernooij, methodologist,30,31

Andrés Viteri-García, methodologist,25,32 Ying Wang, methodologist, pharmacist,1 Liang Yao,

methodologist,1 Zhikang Ye, methodologist, pharmacist,1 Gordon H Guyatt, methodologist,

internist,1,6 Romina Brignardello-Petersen, methodologist1

1 Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main St W, Hamilton, ON L8S 4L8, Canada 2 Evidence Based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou, Gansu, China 3 Servicio de Clinica Médica del Hospital Alemán, Buenos Aires, Argentina 4 Iberoamerican Cochrane Centre, Sant Pau Biomedical Research Institute (IIB Sant Pau), Barcelona, Spain 5 CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain 6 Department of Medicine, McMaster University, Hamilton, ON, Canada 7 Department of Medicine and Centre de recherche du CHU de Sherbrooke, Sherbrooke, Quebec, Canada 8 Department of Preventive Medicine, College of Medicine, Chosun University, Gwangju, Republic of Korea 9 Cochrane China Network Affiliate, Chongqing Medical University, Chongqing, China

Please note that this annex contains the preprint version of this meta-analysis, as used by the Guideline Development Group in its deliberations. The final peer-reviewed version will be available in due course in the BMJ.

ANNEX

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10 School of Public Health and Management, Chongqing Medical University, Chongqing, China 11 Department of Medicine, University of Toronto, Toronto, ON, Canada 12 Division of General Internal Medicine & Division of Clinical Epidemiology, University Hospitals of Geneva, Geneva, Switzerland 13 Department of Anesthesia, McMaster University, Hamilton, ON, Canada 14 William Osler Health Network, Toronto, ON, Canada 15 Norwegian Institute of Public Health, Oslo, Norway 16 Institute of Health and Society, University of Oslo, Oslo, Norway 17 Ted Rogers Center for Heart Research, Toronto General Hospital, ON, Canada 18 Department of Medicine, Western University, London, ON, Canada 19 College of Medical Informatics, Chongqing Medical University, Chongqing, China 20 Wolfson Palliative Care Research Centre, Hull York Medical School, Hull, UK

21 Schwartz/Reisman Emergency Medicine Institute, Sinai Health, Toronto, ON, Canada 22 Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada 23 Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada 24 Department of Medicine, University of Kansas Medical Center, Kansas City, MO, USA 25 Epistemonikos Foundation, Santiago, Chile 26 UC Evidence Center, Cochrane Chile Associated Center, Pontificia Universidad Católica de Chile, Santiago, Chile 27 Hematology and Oncology, Mayo Clinic Rochester, Rochester, MN, USA 28 School of Public Health and Preventative Medicine, Monash University, Melbourne, Australia 29 Department of Medicine, University Health Network, Toronto, ON, Canada 30 Department of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht, Netherlands 31 Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands 32 Centro de Investigación de Salud Pública y Epidemiología Clínica (CISPEC), Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Quito, Ecuador

*Joint first authors

Correspondence to: R Siemieniuk [email protected]

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Abstract

Objective To compare the effects of treatments for coronavirus disease 2019 (covid-19). Design Living systematic review and network meta-analysis. Data sources WHO covid-19 database, a comprehensive multilingual source of global covid-19 literature, up to 27 October 2020 and six additional Chinese databases up to 16 October 2020. Study selection Randomised clinical trials in which people with suspected, probable, or confirmed covid-19 were randomised to drug treatment or to standard care or placebo. Pairs of reviewers independently screened potentially eligible articles. Methods After duplicate data abstraction, a bayesian network meta-analysis was conducted. Risk of bias of the included studies was assessed using a modification of the Cochrane risk of bias 2.0 tool, and the certainty of the evidence using the grading of recommendations assessment, development and evaluation (GRADE) approach. For each outcome, interventions were classified in groups from the most to the least beneficial or harmful following GRADE guidance. Results 85 trials enrolling 41 669 patients met inclusion criteria; 50 (58.8 %) trials and 25 081 (60.2%) patients are new from the previous iteration. 75 randomised controlled trials were eligible for analysis performed on 21 October 2020; 43 (50.6%) of these trials met the threshold inclusion in the analyses with at least 100 participants or 20 events. Compared with standard care, corticosteroids probably reduce death (risk difference 17 fewer per 1000 patients, 95% credible interval 34 fewer to 1 more, moderate certainty), mechanical ventilation (29 fewer per 1000 patients, 54 fewer to 1 more, moderate certainty), and days free from mechanical ventilation (2.6 fewer, 0.2 fewer to 5.0 fewer, moderate certainty). The impact of remdesivir on mortality, mechanical ventilation, length of hospital stay, and duration of symptoms is uncertain, but it probably does not substantially increase adverse effects leading to drug discontinuation (0 more per 1000, 9 fewer to 40 more, moderate certainty). Azithromycin, hydroxychloroquine, lopinavir/ritonavir, interferon-beta, and tocilizumab may not reduce risk of death or have an effect on any other patient-important outcome. The certainty in effects for all other interventions was low or very low. Conclusion Corticosteroids probably reduce mortality and mechanical ventilation in patients with covid-19 compared with standard care, whereas azithromycin, hydroxychloroquine, interferon-beta, and tocilizumab may not reduce either. Whether or not remdesivir confers any patient-important benefit remains uncertain. Systematic review registration This review was not registered. The protocol is included as a supplement. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is the 2nd update of the original article published on 30 July 2020 (BMJ 2020;370:m2980), and previous versions can be found as data supplements.

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Introduction As of 13 November 2020, more than 55 million people have been infected with severe acute

respiratory syndrome coronavirus virus 2 (SARS-CoV-2), the virus responsible for coronavirus

disease 2019 (covid-19); of these, more than 1.3 million have died.1 Despite global efforts to

identify effective interventions for the prevention and treatment of covid-19, which have resulted

in 2400 trials completed or underway,2 evidence for effective treatment remains limited.

Faced with the pressures of a global pandemic, healthcare workers around the world are

prescribing drugs off-label for which there is only very low quality evidence. Timely evidence

summaries and associated guidelines could ameliorate the problem.3 Clinicians, patients,

guideline bodies, and government agencies are also facing the challenges of interpreting the

results from trials that are being published at a rate never encountered previously. This

environment makes it necessary to produce well developed summaries that distinguish more

trustworthy evidence from less trustworthy evidence.

Living systematic reviews deal with the main limitation of traditional reviews—that of

providing an overview of the relevant evidence only at a specific time.4 This is crucial in the

context of covid-19, in which the best evidence is constantly changing. The ability of a living

network meta-analysis to present a complete, broad, and updated view of the evidence makes it

the best type of evidence synthesis to inform the development of practice recommendations.

Network meta-analysis, rather than pairwise meta-analysis, provides useful information about the

comparative effectiveness of treatments that have not been tested head to head. The lack of such

direct comparisons is certain to limit inferences in the covid-19 setting. Moreover, the

incorporation of indirect evidence can strengthen evidence in comparisons that were tested head

to head.5

In this living systematic review and network meta-analysis we compare the effects of drug

treatments for covid-19. This review is part of the BMJ Rapid Recommendations project, a

collaborative effort from the MAGIC Evidence Ecosystem Foundation (www.magicproject.org)

and The BMJ.6 This living systematic review and network meta-analysis will directly inform

BMJ Rapid Recommendations6 on covid-19 treatments, initiated to provide trustworthy,

actionable, and living guidance to clinicians and patients soon after new and potentially practice-

changing evidence becomes available. The first covid-19 BMJ Rapid Recommendation

considered the role of remdesivir7 and subsequent WHO living guidance considered

corticosteroids8 (box 1). This living network meta-analysis is the third version. The previous

versions are available in the supplementary material.

Box start Box 1 Linked resources in this BMJ Rapid Recommendations cluster • Rochwerg B, Agarwal A, Zeng L, et al. Remdesivir for severe covid-19: a clinical practice

guideline. BMJ 2020;370:m2924, doi:10.1136/bmj.m2924

- Rapid Recommendation on remdesivir for covid-19 • Lamontagne F, Agoritsas T, Macdonald H, et al. A living WHO guideline on drugs for covid-

19. BMJ 2020;370:m3379, doi:10.1136/bmj.m3379 - Living WHO BMJ Rapid Recommendations guidance on drugs for covid-19

• World Health Organization. Corticosteroids for COVID-19. Living guidance 2 September 2020. https://www.who.int/publications/i/item/WHO-2019-nCoV-Corticosteroids-2020.1

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• Siemieniuk RAC, Bartoszko JJ, Ge L, et al. Drug treatments for covid-19: living systematic review and network meta-analysis. BMJ 2020;370:m2980, doi:10.1136/bmj.m2980 - Review and network meta-analysis of all available randomised trials that assessed drug

treatments for covid-19 • MAGICapp (https://app.magicapp.org/#/guideline/j1W7rn)

- Expanded version of the methods, processes, and results with multilayered recommendations, evidence summaries, and decision aids for use on all devices

Box end

Methods A protocol provides the detailed methods of this systematic review, including all updates (see

supplementary file). We report this living systematic review following the guidelines of the

preferred reporting items for systematic reviews and meta-analyses (PRISMA) checklist for

network meta-analyses.9 A living systematic review is a cumulative synthesis that is updated

regularly as new evidence becomes available.10 The linked BMJ Rapid Recommendations

guideline panels approved all major decisions relevant to data synthesis.

Eligibility criteria We included randomised clinical trials in people with suspected, probable, or confirmed

covid-19 that compared drugs for treatment against one another or against no intervention,

placebo, or standard care. We included trials regardless of publication status (peer reviewed, in

press, or preprint) or language. No restrictions were applied based on severity of illness or setting

and we included trials of Chinese medicines if the drug comprised one or more specific

molecules with a defined molecular weight dosing.

We excluded randomised controlled trials evaluating vaccination, blood products, nutrition,

traditional Chinese herbal medicines that include more than one molecule or a molecule without

specific molecular weighted dosing, and non-drug supportive care interventions. Trials that

evaluated these interventions were identified and categorised separately.

Information sources We perform daily searches from Monday to Friday in the World Health Organization (WHO)

covid-19 database for eligible studies – a comprehensive multilingual source of global literature

on covid-19. Prior to its merge with the WHO covid-19 database on 9 October 2020, we

performed daily searches from Monday to Friday in the US Centers for Disease Control and

Prevention (CDC) COVID-19 Research Articles Downloadable Database for eligible studies.11

The database includes, but is not limited to the following 25 bibliographic and grey literature

sources: Medline (Ovid and PubMed), PubMed Central, Embase, CAB Abstracts, Global Health,

PsycInfo, Cochrane Library, Scopus, Academic Search Complete, Africa Wide Information,

CINAHL, ProQuest Central, SciFinder, the Virtual Health Library, LitCovid, WHO covid-19

website, CDC covid-19 website, Eurosurveillance, China CDC Weekly, Homeland Security

Digital Library, ClinicalTrials.gov, bioRxiv (preprints), medRxiv (preprints), chemRxiv

(preprints), and SSRN (preprints).

The daily searches are designed to match the update schedule of the database and to capture

eligible studies the day of or the day after publication. To identify randomised controlled trials,

we filtered the results from the CDC’s database through a validated and highly sensitive machine

learning model.12 We tracked preprints of randomised controlled trials until publication and

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updated data to match that in the peer reviewed publication when discrepant and reconciled

corrections and retractions existed.

In addition, we search six Chinese databases monthly: Wanfang, Chinese Biomedical

Literature, China National Knowledge Infrastructure, VIP, Chinese Medical Journal Net

(preprints), and ChinaXiv (preprints). We adapted the search terms for covid-19 developed by

the CDC to the Chinese language. For the Chinese literature search, we also included search

terms for randomised trials. The supplementary file includes the Chinese literature search

strategy.

We monitor living evidence retrieval services on an ongoing basis. These included the Living

Overview of the Evidence (L-OVE) COVID-19 Repository by the Epistemonikos Foundation13

and the Systematic and Living Map on COVID-19 Evidence by the Norwegian Institute of

Public Health, in collaboration with the Cochrane Canada Centre at McMaster University.14

We searched all English information sources from 1 December 2019 to 27 October 2020, and

the Chinese literature from conception of the databases to 16 October 2020.

Study selection Using a systematic review software, Covidence,15 pairs of reviewers, following training and

calibration exercises, independently screened all titles and abstracts, followed by full texts of

trials that were identified as potentially eligible. A third reviewer adjudicated conflicts.

Data collection For each eligible trial, pairs of reviewers, following training and calibration exercises,

extracted data independently using a standardised, pilot tested data extraction form. Reviewers

collected information on trial characteristics (trial registration, publication status, study status,

design), patient characteristics (country, age, sex, smoking habits, comorbidities, setting and type

of care, and severity of covid-19 symptoms for studies of treatment), and outcomes of interest

(means or medians and measures of variability for continuous outcomes and the number of

participants analysed and the number of participants who experienced an event for dichotomous

outcomes). Reviewers resolved discrepancies by discussion and, when necessary, with

adjudication by a third party. We updated the data collected from included preprints as soon as

the peer review publication became available.

Outcomes of interest were selected based on importance to patients and were informed by

clinical expertise in the systematic review team and in the linked guideline panel responsible for

the WHO-BMJ Rapid Recommendations.8 The panel includes unconflicted clinical and

methodology experts, recruited to ensure global representation, and patient-partners. All panel

members rated outcomes from 1 to 9 based on importance to individual patients (9 being most

important), and we included any outcome rated 7 or higher by any panel member. Selected

outcomes included mortality (closest to 90 days), mechanical ventilation (total number of

patients, over 90 days), adverse events leading to discontinuation (within 28 days), viral

clearance (closest to 7 days, 3 days either way), admission to hospital, duration of hospital stay,

intensive care unit (ICU) length of stay, duration of mechanical ventilation, time to symptom

resolution or clinical improvement, time to viral clearance, and days free from mechanical

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ventilation (within 28 days). Viral clearance at seven days and time to viral clearance were

included because both may be surrogates for transmissibility, although this is uncertain.16

Because of the inconsistent reporting observed across trials, we used a hierarchy for the

outcome mechanical ventilation in which we considered the total number of patients who

received ventilation over the study, if available, and the number of patients ventilated at the time

point at which most of the patients were mechanically ventilated, if that is the only way in which

this outcome was reported.

Risk of bias within individual studies For each eligible trial, reviewers, following training and calibration exercises, used a revision

of the Cochrane tool for assessing risk of bias in randomised trials (RoB 2.0)17 to rate trials as

either at i) low risk of bias, ii) some concerns—probably low risk of bias, iii) some concerns—

probably high risk of bias, or iv) high risk of bias, across the following domains: bias arising

from the randomisation process; bias owing to departures from the intended intervention; bias

from missing outcome data; bias in measurement of the outcome; bias in selection of the

reported results, including deviations from the registered protocol; bias due to competing risks;

and bias arising from early termination for benefit. We rated trials at high risk of bias overall if

one or more domains were rated as some concerns—probably high risk of bias or as high risk of

bias and as low risk of bias if all domains were rated as some concerns—probably low risk of

bias or low risk of bias. Reviewers resolved discrepancies by discussion and, when not possible,

with adjudication by a third party.

Data synthesis We conducted the network meta-analysis using a bayesian framework.18 In this report, we

conducted a network meta-analysis of drug treatments for covid-19 that included all patients,

regardless of severity of disease.

Summary measures

We summarised the effect of interventions on dichotomous outcomes using the odds ratio

and corresponding 95% credible interval. For continuous outcomes, we used the mean difference

and corresponding 95% credible interval in days for ICU length of stay, length of hospital stay,

and duration of mechanical ventilation because we expected similar durations across randomised

controlled trials. For time to symptom resolution and time to viral clearance, we first performed

the analyses using the relative effect measure ratio of means and corresponding 95% credible

interval before calculating the mean difference in days because we expected substantial variation

between studies.19

Treatment nodes

Treatments were grouped into common nodes based on molecule and not on dose or

duration. For intervention arms with more than one drug, we created a separate node.

Chloroquine and hydroxychloroquine were included in the same node for covid-19 specific

effects and separated for disease independent adverse effects. We drew network plots using the

networkplot command of Stata version 15.1 (StataCorp, College Station, TX), with thickness of

lines between nodes and size of the nodes based on the inverse of the variance of the direct

comparison.20

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Statistical analysis

For most outcomes, we conducted network meta-analyses and pairwise meta-analyses using a

bayesian framework with the same priors for the variance and effect parameters.18 In previous

versions, we used fixed effects for some outcomes because data was sparse or dominated by a

single trial. In this update, we used random effects for all outcomes. We used a plausible prior

for variance parameter and a uniform prior for the effect parameter suggested in a previous study

based on empirical data.21 For all analyses, we used three Markov chains with 100�000

iterations after an initial burn-in of 10�000 and a thinning of 10. We used node splitting models

to assess local incoherence and to obtain indirect estimates.22 All network meta-analyses were

performed using the gemtc package of R version 3.6.3 (RStudio, Boston, MA)23 and all pairwise

meta-analyses using the bayesmeta package.18

In the first iteration of this living network meta-analysis, some treatment nodes with few total

participants and few total events resulted in highly implausible and extremely imprecise effect

estimates. We therefore decided to include only treatments that included at least 100 patients or

had at least 20 events, based on our impression of the minimum number of patients/events to

possibly provide meaningful results.

Certainty of the evidence We assessed the certainty of evidence using the grading of recommendations assessment,

development and evaluation (GRADE) approach for network meta-analysis.5 24 25 Two people

with experience in using GRADE rated each domain for each comparison separately and

resolved discrepancies by consensus. We rated the certainty for each comparison and outcome as

high, moderate, low, or very low, based on considerations of risk of bias, inconsistency,

indirectness, publication bias, intransitivity, incoherence (difference between direct and indirect

effects), and imprecision.25 Judgments of imprecision for this systematic review were made using

a minimally contextualised approach, with a null effect as the threshold of importance.26 The

minimally contextualised approach considers only whether credible intervals include the null

effect and thus does not consider whether plausible effects, captured by credible intervals,

include both important and trivial effects.26 To evaluate certainty of no benefit (or no effect), we

used a 2% risk difference threshold of the 95% credible interval for mortality and mechanical

ventilation. In other words, if the entire 95% credible interval was within 2% of the null effect,

we would not rate down for imprecision. We decided on this preliminary threshold based on a

survey of the authors. In future updates, it will be guided by a survey of patients and guideline

panellists. We created GRADE evidence summaries (Summary of Findings tables) in the

MAGIC Authoring and publication platform (www.magicapp.org) to provide user friendly

formats for clinicians and patients and to allow re-use in the context of clinical practice

guidelines for covid-19.

Interpretation of results To facilitate interpretation of the results, we calculated absolute effects for outcomes in

which the summary measure was an odds ratio or ratio of means. When available, we inferred

baseline risk in the usual care group for each outcome from representative observational data

(supplementary material). For mortality, we used data from the CDC on patients who were

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hospitalized with covid-19.27 28 For mechanical ventilation, duration of invasive mechanical

ventilation, duration of hospitalization, and ICU length of stay we used baseline risks from the

International Severe Acute Respiratory and Emerging Infection COVID-19 database.29 For all

other outcomes, we used the median from all studies in which participants received standard of

care to calculate the baseline risk for each outcome, with each study weighed equally. We

calculated absolute effects using the transitive risks model30 using R2jags package in R.31

For each outcome, we classified treatments in groups from the most to the least effective

using the minimally contextualised framework, which focuses on the treatment effect estimates

and the certainty of the evidence.25

Subgroup and sensitivity analysis Subgroup analyses were performed for specific interventions of interest at the direction of the

linked WHO living guideline panel.8 In this iteration, we performed subgroup analyses for

remdesivir, hydroxychloroquine, and lopinavir/ritonavir. The panel requested subgroup analyses

by age (children vs. non-elderly adults vs. elderly) and severity (non-severe vs. severe vs.

critical). We performed bayesian hierarchical meta-regression with study as a random effect.

Patient and public involvement Patients were involved in outcome selection, interpretation of results, and the generation of

parallel recommendations, as part of the BMJ Rapid Recommendations initiative.

Results After screening 15 130 titles and abstracts and 300 full texts, 106 unique randomised

controlled trials from 94 publications were identified that evaluated drug treatments as of 27

October 2020 (fig 1).32-76 Searches of living evidence retrieval services identified 19 additional

eligible randomised controlled trials.77-81 Fifty-nine randomized trials have been published in

peer reviewed journals, 28 only as preprints, and 17 within two meta-analyses. Most of the trials

were registered (98/106; 93%), published in English (89/94; 95%), and evaluated treatment in

patients admitted to hospital with covid-19 (95/106; 90%). Just over one third of the trials were

conducted in China (33/106; 39%), with the remainder distributed globally. Of the 106 included

drug trials, the three most commonly studied drugs were (hydroxy)chloroquine (31/106; 29%),

followed by corticosteroids (11/106; 10%) and lopinavir/ritonavir (8/106; 7.5%). Several

randomised controlled trials were not eligible to be included in the analysis: four trials that

evaluated different durations or doses of the same drug, because both arms would have been

classified within the same treatment node35 43 60 82; two trials with insufficient data83-85; and four

trials that reported no outcomes of interest.81 86-88 Seventy-five randomised controlled trials were

eligible for the analysis. To mitigate results with highly implausible and extremely imprecise

estimates, we included 43 (57.3%) of these trials reporting on treatments with at least 100

patients or 20 events. Table 1 presents the characteristics of the included studies. Additional

study characteristics, outcome data, and risk of bias assessments for each study are available in

the supplementary file.

Of the randomised controlled trials included in the analyses, seven did not have publicly

accessible protocols or registrations.66 79 86 88-91 Of the trials with publicly accessible protocols or

registrations, 55 reported results for one or more of our outcomes of interest that were not

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prespecified in protocols or registrations. No other discrepancies between the reporting of our

outcomes of interest in trial reports and protocols or registrations were noted. One trial did not

report outcomes in the groups as randomised; the authors shared outcome data with us in the

groups as randomised.51

Eleven studies were initially posted as preprints and subsequently published after peer

review.35 47 92 48 72 81 93-97 The supplementary material presents the differences between study

preprint and peer reviewed publications. Six studies had discrepancies in outcome reporting

between the preprint and peer-reviewed publication. No substantiative differences were found for

the other five studies.

All analyses reached convergence based on trace plots and a Brooks-Gelman-Rubin statistic

less than 1.05, except comparisons including umifenovir for mortality and tocilizumab for

adverse events leading to discontinuation because no patients randomised to either of these drugs

died.

Risk of bias in included studies The supplementary material presents the assessment of risk of bias of the included studies for

each outcome. Nine studies were judged at low risk of bias in all domains.17 33 35 40 56 61 85 98 99 All

other studies had probably high or high risk of bias in at least one of the domains.

Effects of the interventions The supplementary material presents the network plots depicting the interventions included

in the network meta-analysis of each outcome. Figure 2 presents a summary of the effects of the

interventions on the outcomes. The supplementary file also presents detailed relative and

absolute effect estimates and certainty of the evidence for all comparisons and outcomes. We did

not detect statistical incoherence in any of the network meta-analyses.

Mortality

Seventy-two randomised controlled trials including 40 083 participants reported mortality

(Table 1). Thirty-eight trials with 37 730 participants met the threshold of analysing treatments

with a minimum of 100 patients or 20 events and were included in the network-meta analysis.

The treatment nodes included were azithromycin, colchicine, corticosteroids, favipiravir,

hydroxychloroquine, hydroxychloroquine plus azithromycin, interferon beta, lopinavir-ritonavir,

remdesivir, rhG-CSF, tocilizumab, umifenovir, and standard care. Random effects network meta-

analysis showed that corticosteroids (odds ratio 0.85, 95% credible interval 0.71 to 1.01; risk

difference 17 fewer per 1000, 95% credible interval 34 fewer to 1 more; moderate certainty)

probably reduce deaths compared to standard care (fig 2). Evidence was less certain for

remdesivir (odds ratio 0.90, 0.70 to 1.12; risk difference 12 fewer per 1000, 35 fewer to 14 more;

low certainty) and lopinavir-ritonavir (odds ratio 0.90, 0.73 to 1.09; risk difference 12 fewer per

1000, 31 fewer to 10 more; low certainty). Patients randomised to hydroxychloroquine (odds

ratio 1.10, 0.90 to 1.35; risk difference 11 more per 1000, 11 fewer to 38 more; low certainty of

no benefit) and interferon beta (odds ratio 1.02, 0.70 to 1.32; risk difference 2 more per 1000, 35

fewer to 35 more; low certainty) did not have a lower risk of death than those randomised to

standard care. 95% credible intervals included both substantial benefit and harm for

azithromycin, colchicine, favipiravir, hydroxychloroquine plus azithromycin, and tocilizumab

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(all very low certainty).Very low certainty evidence suggests that rhG-CSF may reduce risk of

death compared to standard care. Fixed effects network meta-analysis led to similar results for all

treatments compared with standard care: corticosteroids (odds ratio 0.86, 0.77 to 0.95),

hydroxychloroquine (odds ratio 1.08, 0.96 to 1.22), interferon beta (odds ratio 1.08, 0.89 to

1.30), lopinavir-ritonavir (odds ratio 0.89, 0.80 to 1.00) and remdesivir (odds ratio 0.92, 0.80 to

1.07).

Mechanical ventilation

Forty randomised controlled trials including 33 727 participants reported mechanical

ventilation (Table 1). Twenty-one trials with 32 162 participants met the threshold of analysing

treatments with a minimum of 100 patients or 20 events and were included in the network-meta

analysis. The treatment nodes included were azithromycin, corticosteroids, hydroxychloroquine,

hydroxychloroquine plus azithromycin, interferon beta, lopinavir-ritonavir, remdesivir, rhG-CSF,

and standard care (fig 2). Random effects network-meta analysis showed that, compared with

standard care, corticosteroids probably reduce risk of mechanical ventilation (odds ratio 0.72,

0.50 to 1.01; risk difference 29 fewer per 1000, 54 fewer to 1 fewer; moderate certainty for risk

of bias). Certainty was lower for remdesivir (odds ratio 0.68, 0.41 to 1.00; risk difference 33

fewer per 1000, 65 fewer to 1 more; low certainty) and hydroxychloroquine (odds ratio 1.20,

0.83 to 1.81; risk difference 20 more per 1000, 18 fewer to 76 more; low certainty for risk of bias

and imprecision). Evidence for was less certain for azithromycin, hydroxychloroquine plus

azithromycin, interferon beta, lopinavir-ritonavir, and tocilizumab, and rhG-CSF (all very low

certainty). Fixed effects network meta-analysis led to similar results for all treatments compared

with standard care: corticosteroids (odds ratio 0.73, 0.61 to 0.86), remdesivir (odds ratio 0.88,

0.76 to 1.03), hydroxychloroquine (odds ratio 1.16, 0.97 to 1.38), azithromycin (odds ratio 1.13,

0.79 to 1.64), hydroxychloroquine plus azithromycin (odds ratio 1.59, 0.86 to 2.89), interferon

beta (odds ratio 0.97, 0.80 to 1.18), and lopinavir-ritonavir (odds ratio 1.16, 0.98 to 1.36).

Adverse events leading to discontinuation

Thirty-two randomised controlled trials including 4698 participants reported adverse effects

leading to discontinuation of the study drug (Table 1). Six trials with 1946 participants met the

threshold of analysing treatments with a minimum of 100 patients or 20 events and were

included in the network-meta analysis. The treatment nodes included were hydroxychloroquine,

remdesivir, tocilizumab, and standard care. Moderate certainty evidence showed that remdesivir

did not result in a substantial increase in adverse effects leading to drug discontinuation

compared with standard care (odds ratio 1.00, 0.37 to 3.83; risk difference 0 more per 1000, 9

fewer to 40 more). Certainty in evidence for hydroxychloroquine and tocilizumab was very low

(fig 2).

Viral clearance at 7 days (3 days either way)

Twenty-four randomised controlled trials including 1857 participants measured viral

clearance with polymerase chain reaction cut-off points (Table 1). Fourteen trials with 1186

participants met the threshold of analysing treatments with a minimum of 100 patients or 20

events and were included in the network-meta analysis. The treatment nodes included were

corticosteroids, favipiravir, hydroxychloroquine, interferon gamma, interferon kappa plus trefoil

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factor 2, lopinavir-ritonavir, remdesivir, and standard care. We did not find any convincing

evidence that any of the interventions increased the rate of viral clearance (fig 2). The certainty

of the evidence was low for remdesivir compared with standard care, and very low for all other

comparisons.

Admission to hospital

Three randomised controlled trials including 603 participants reported admission to hospital

in patients who were outpatients at baseline (Table 1). One study of hydroxychloroquine versus

placebo was included.98 There were too few events to make any inferences (odds ratio 0.39, 0.12

to 1.28; risk difference 26 fewer per 1000, 38 fewer to 12 more; low certainty) (fig 2).

Venous thromboembolism

One study including 20 participants100 reported venous thromboembolism in patients who

received an anticoagulant as the active drug. No treatment node contained information on at least

100 patients, therefore no analyses were conducted for this outcome.

Clinically-important bleeding

One study including 20 participants100 reported clinically-important bleeding in patients who

received an anticoagulant as the active drug. No treatment node contained information on at least

100 patients, therefore no analyses were conducted for this outcome.

Duration of hospital stay

Thirty-nine randomised controlled trials including 22 807 participants reported duration of

hospital stay (Table 1). Twenty trials with 21 440 participants meeting the threshold of analysing

treatments with a minimum of 100 patients or 20 events were included in the network meta-

analysis. The treatment nodes included were azithromycin, colchicine, corticosteroids,

hydroxychloroquine, hydroxychloroquine plus azithromycin, lopinavir-ritonavir, remdesivir,

rhG-CSF, tocilizumab, and standard care. Compared with standard care, duration of

hospitalisation was shorter in patients who received colchicine (mean difference −1.57 days,

−2.78 to −0.32; low certainty). There was no evidence that azithromycin (very low certainty),

corticosteroids (very low certainty), hydroxychloroquine (very low certainty),

hydroxychloroquine plus azithromycin (low certainty), lopinavir-ritonavir (low certainty),

tocilizumab (very low certainty), or remdesivir (low certainty), rhG-CSF (low certainty) impact

length of stay (fig 2).

ICU length of stay

Nine randomised controlled trials including 890 participants reported length of ICU stay

(Table 1). Two studies randomised at least 100 patients to receive corticosteroids.70 80 Compared

with standard care, length of ICU stay was shorter in patients who received corticosteroids (mean

difference −3.83 days, −5.88 to −1.78; low certainty) (fig 2).

Duration of mechanical ventilation

Six randomised controlled trials including 857 participants reported duration of mechanical

ventilation (Table 1). Three studies with 739 participants meeting the threshold of analysing

treatments with a minimum of 100 patients or twenty events were included in the network-meta

analysis. The treatment nodes included corticosteroids, remdesivir, and standard care. There was

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no evidence that corticosteroids (mean difference −1.41 days, −3.44 to 0.62; low certainty) and

remdesivir (mean difference −1.28 days, −4.06 to 1.47; low certainty) reduce duration of

mechanical ventilation (fig 2).

Ventilator-free days

Five randomised controlled trials including 1036 participants reported ventilator-free days

(Table 1). Three studies with 962 participants meeting the threshold of analysing treatments with

a minimum of 100 patients were included in the network-meta analysis. The treatment nodes

included were azithromycin, corticosteroids, tocilizumab, and standard care. Compared to

standard care, corticosteroids (mean difference 2.62 days, 0.24 to 4.97; moderate certainty) may

increase ventilator-free days. There was no evidence that tocilizumab (low certainty) and

azithromycin (very low certainty) increase ventilator-free days (fig 2).

Time to symptom resolution

Thirty-two randomised controlled trials including 4424 participants reported time to

symptom resolution (Table 1). Thirteen trials including 3285 participants meeting the threshold

of analysing treatments with a minimum of 100 patients were analysed. The treatment nodes

included were hydroxychloroquine, lopinavir-ritonavir, remdesivir, rhG-CSF, tocilizumab, and

standard care. There was no evidence that remdesivir (moderate certainty), hydroxychloroquine (

low certainty), and lopinavir-ritonavir (low certainty) led to shorter symptom duration than in

patients who received standard care (fig 2).

Time to viral clearance

Twenty-two randomised controlled trials including 1459 participants reported time to viral

clearance (Table 1). At least 100 patients across five trials received hydroxychloroquine and

standard care. The certainty of the evidence was very low (fig 2).

Subgroups

Remdesivir have different effects in patients by severity of disease (ratio of odds ratios

(ROR) 1.80, CI 1.27 2.59, probability of ROR≤1 = 0.0003). The effects of remdesivir on

mortality the three subgroups are: non-severe disease (OR 0.71, 0.33 to 1.46), severe disease

(OR 0.73, 0.49 to 1.03), critical disease (OR 1.30, 0.89 to 1.97). Using established criteria, we

felt that this subgroup effect had low-to-moderate credibility (supplementary material). No other

subgroup was notable for any subgroup effects.

Discussion This living systematic review and network meta-analysis provides a comprehensive overview

of the evidence for drug treatments of covid-19 up to 21 October 2020 and a comprehensive list

of drug trials to 27 October 2020. The certainty of the evidence for most of the comparisons was

very low. Corticosteroids probably reduce the risk of death, mechanical ventilation, and increase

ventilator-free days results driven almost entirely by the RECOVERY trial.50

Whether or not remdesivir has any effect on mortality is uncertain. If one believes the

subgroup effect, remdesivir may reduce or have no effect mortality in patients with non-critical

disease and may increase or have no effect on mortality in patients with critical illness. The

subgroup effect however has only moderate credibility and whether or not remdesivir reduces or

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increases mortality in any subgroup is uncertain. Direct evidence from randomised controlled

trials in patients with covid-19 has so far provided little definitive evidence about adverse effects

for most interventions, apart from remdesivir which probably has low risk for adverse effects

leading to discontinuation.

No other drug was found to have an impact on any patient with at least moderate certainty for

any other outcome. Based on three small trials, colchicine may reduce duration of hospitalization

(low certainty) and based on a single small trial, rhG-CSF might reduce mortality and

mechanical ventilation in patients with lymphopenia (low certainty).

Compared with the second iteration, there are several important updates (box 2). We now

have evidence from several large scale international trials on remdesivir, hydroxychloroquine,

lopinavir/ritonavir, and interferon beta. Unfortunately, the trials showed that none of these

interventions had a meaningful effect on any patient important outcomes.

Box start Box 2. Summary of changes since last iteration • Fifty-seven additional randomised trials (25 081 participants) • Azithromycin, colchicine, interferon beta, interferon gamma, interferon kappa plus trefoil

factor 2, rhG-CSF, tocilizumab are new interventions included in the analyses, but certainty is low or very low for the effects of these interventions

• We changed the previous analyses that were performed in fixed effects to random effects • New evidence suggests that remdesivir may not reduce mortality (low certainty) or time to

symptom resolution (moderate certainty). Previously, the evidence suggested a benefit on these outcomes with remdesivir.

• New evidence that glucocorticoids probably reduce length of ICU stay (low certainty) and increase ventilator-free days (moderate certainty)

• Evidence for other interventions is similar to the previous version Box end

Strengths and limitations of this review Our search strategy and eligibility criteria were comprehensive, without restrictions on

language of publication or publication status. To ensure expertise in all areas, our team is

composed of clinical and methods experts who have undergone training and calibration exercises

for all stages of the review process. To minimise problems with counterintuitive results, we

anticipated challenges that arise in network meta-analysis when data are sparse.20 Many of the

results for comparisons with sparse data were uninformative and were sometimes implausible.

For that reason, we decided to report evidence on treatments for which at least 100 people were

randomised or for which there were at least 20 events. In the future, when more data from more

treatments are available, our classification of interventions from the most to the least effective

will facilitate clear interpretation of results.

The main limitation of the data is that only nine studies were judged to be at low risk of bias.

The primary limitation of the evidence is lack of blinding, which might introduce bias through

differences in co-interventions between randomisation groups. We chose to consider the

treatment arms that did not receive an active experimental drug (ie, placebo or standard care)

within the same node: it is possible that the unblinded standard care groups received

systematically different co-interventions than groups randomised to receive a placebo. Direct

comparisons in which the evidence is dominated by unblinded studies were rated down,

consistent with GRADE, for risk of bias and that is reflected in the rating of the quality of

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evidence from the network estimate.101 Many of the data also had reporting concerns. For some

outcomes, the method in which the researchers measured and reported outcomes proved

inconsistent across studies. This led the team to propose a hierarchy for the outcome mechanical

ventilation, as described in the methods.

The living nature of our systematic review and network meta-analysis could conceivably (at

least temporarily) amplify publication bias, because studies with promising results are more

likely to be published and are published sooner than studies with negative results. The inclusion

of preprints, many of which have negative results, might mediate this risk. Industry sponsored

trials such as those for remdesivir and other patented drugs could be particularly at risk of

publication bias, and positive results for these drugs might require more cautious interpretation

than generic drugs tested in randomised controlled trials independent of industry influence.

However, the inclusion of preprints in our network meta-analysis might introduce bias from

simple errors and the reporting limitations of preprints. We include preprints because of the

urgent need for information and because so many of the studies on covid-19 are published first as

preprints. So far, there did not appear to be any major differences between preprints and peer-

reviewed manuscripts.

Our living systematic review and network meta-analysis will continue to inform the

development of the WHO living guidelines and BMJ Rapid Recommendations.8 An important

difference in the methods for assessing the certainty of the evidence does, however, exist

between the two. In this living systematic review and network meta-analysis, we use a minimally

contextualised approach for rating the certainty of the evidence, whereas the guideline panels use

a fully contextualised approach in which the thresholds of importance of magnitudes of effects

depend on all other outcomes and factors involved in the decision.26 The contextualisation

explains differences in the certainty of the evidence between the two. The limitations of

potentially misleading results when the network is sparse, and the desirability of focusing on

direct estimates from larger studies when this is the case, explain differences in the details of the

estimates of effect in this network meta-analysis and in the associated guidelines for remdesivir.7

8

To date, we are aware of two other similar efforts to ours.102 103 We decided to proceed

independently to ensure that the results fully inform clinical decision making for the associated

living guidance.8 We also include a more comprehensive search for the evidence and several

differences in analytical methods, which we believe are best suited for this process. It is also

important to evaluate the reproducibility and replicability of results from different scientific

approaches.

We will periodically update this living systematic review and network meta-analysis. We

from several new randomised trials that examined tocilizumab were published after our statistical

analysis and trials on all drugs are being published at an increasingly faster rate. The changes

from each version will be highlighted for readers and the most updated version will be the one

available in the publication platform. Previous versions will be archived in the supplementary

material. This living systematic review and network meta-analysis will also be accompanied by

an interactive infographic and a website for users to access the most updated results in a user-

friendly format (magicapp.org).

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Conclusions Evidence from this living systematic review and network meta-analysis suggests that

corticosteroids probably reduce mortality, mechanical ventilation, and ventilator-free days in

patients with severe covid-19. Whether or not remdesivir has any impact on any outcome

remains uncertain. Hydroxychloroquine, lopinavir/ritonavir, and interferon beta may not reduce

mortality or mechanical ventilation, and they seem unlikely to have any other benefits. The

effects of most drug interventions are currently highly uncertain, and no definitive evidence

exists that other interventions result in important benefits and harms for any outcomes.

Box start What is already known on this topic Despite huge efforts to identify effective drug interventions for coronavirus disease 2019 (covid-

19), evidence for effective treatment remains limited What this study adds This living systematic review and network meta-analysis provides a comprehensive overview

and assessment of the evidence published as of 21 October 2020 and will be updated periodically

The certainty of the evidence for most interventions tested thus far is low or very low In patients with severe covid-19, glucocorticoids probably decrease mortality, mechanical

ventilation. No other drug has compelling evidence of benefit. Box end

We thank Kevin Cheung and Paul Alexander (who was an author in the previous version of this review) for input and early contributions. We also thank Wim Weber, Helen Macdonald, Navjoyt Ladher, and Rafael Perera-Salazar for editorial contributions to earlier versions of this manuscript.

Contributors: RACS, JJB, LG, and DZ contributed equally to the systematic review and are joint first authors. RACS, JJB, DZ, LG, and RB-P were the core team leading the systematic review. JJB, RC, SAF, MG, BL, RWMV, SM, YW, ZY, IR, AD, TD, AI, AQ, CS, LY, FF, QL, XH, LS, BF, and AV-G identified and selected the studies. DZ, EK, NS, RWMV, AA, YW, KH, HP-H, MAH, CF, SLM, QL, AS, AQ, LY, and FF collected the data. LG, AK, BS, LH, QI, DH-A, GHG, GT, and LT analysed the data. RB-P, HPH, AI, RAM, TD, NS, and DC assessed the certainty of the evidence. SLM, FL, BR, TA, POV, GHG, MM, JDN, ML, TT, BT, FF, and GR provided advice at different stages. RACS, RB-P, and GHG drafted the manuscript. All authors approved the final version of the manuscript. RACS is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funder: This study was supported by the Canadian Institutes of Health Research (grant CIHR-IRSC:0579001321).

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from the Canadian Institutes of Health Research; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not applicable. All the work was developed using published data.

Data sharing: No additional data available.

RACS affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

Dissemination to participants and related patient and public communities: The infographic and MAGICapp decision aids (available at www.magicapp.org/) were created to facilitate conversations between healthcare providers and patients or their surrogates. The MAGICapp decision aids were co-created with people who have lived experience of covid-19.

Table 1 Study characteristics Study Publication status,

registration No No of

participants Country Mean

age (years)

Men (%)

Type of care, comorbidities

Severity Mechanical ventilation at baseline

(%)

Treatments (dose and duration)

Outcomes

Abbaspour Kasgari 202074‡

Published, IRCT20200328046886N1

48 Iran 52.5 37.5 Inpatient; ischemic heart disease (22.9%); diabetes (37.5%); chronic obstructive pulmonary disease (2.1%)

Mild/moderate (100%)

NR Sofosbuvir-daclatasvir (400 mg and 60 mg once daily for 14 days, ribavirin (600 mg twice daily for 14 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; intensive care unit length of stay; duration of ventilation

Abd-Elsalam 202054

Published, NCT04353336 194 Egypt 40.7 58.8 Inpatient NR NR Hydroxychloroquine (200 mg twice daily for 15 days); standard care

Mortality; mechanical ventilation; duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Altay 2020104‡ Preprint, NCT04573153 100 Turkey 35.6 60.0 Outpatient; diabetes (5.0%); hypertension (2.0%)


0 Serine (24 g/day total, given twice daily, for 14 days), N-acetylcysteine (5.1 g/day total, given twice daily, for 14 days), nicotinamide riboside (2 g/day total, given twice daily, for 14 days), L-carnitine tartrate (7.46 g/day total, given twice daily, for 14 days); placebo

Mortality; time to symptom or clinical improvement

Angus 2020; REMAP-CAP70

Published, NCT02735707 403 Australia, Canada, Ireland, France, Netherlands, New Zealand, UK, USA

59.9 71.1 Inpatient; intensive care (100%); cardiovascular disease (7.3%); diabetes (32.1%); asthma or chronic obstructive pulmonary disease (16.2%); respiratory disease (19.5%)

Severe (100%) 100 Hydrocortisone (50 mg four times daily for 7 days); hydrocortisone (50 mg four times daily while in shock for up to 28 days); standard care

Mortality; mechanical ventilation; duration of hospital stay; ICU length of stay

Ansarin 2020105‡ Published, IRCT202003117046797N4

78 Iran 59.8 55.1 Inpatient; diabetes (33.3%); hypertension (50.0%)

NR NR Bromhexine hydrochloride (8 mg three times daily for 14 days); standard care

Mortality; mechanical ventilation; duration of hospital stay

Beigel 2020; ACTT-1106

Published, NCT04280705 1062 USA, Denmark, UK, Greece, Germany, Korea, Mexico, Spain, Japan, Singapore

58.9 64.4 Inpatient; coronary artery disease (11.9%); congestive heart failure (5.6%); diabetes (30.6%); hypertension (50.7%); asthma (11.4%); chronic oxygen requirement (2.2%); chronic respiratory disease (7.6%)

Severe (90.1%) 45.0 Remdesivir (100 mg/day for 10 days); placebo

Mortality; mechanical ventilation; adverse effects leading to discontinuation; duration of hospital stay; duration of ventilation; time to symptom or clinical improvement

Cao 2020; LOTUS China37

Published, ChiCTR2000029308 199 China 58.0 60.3 Inpatient; cerebrovascular disease (6.5%); diabetes (11.6%)

Severe (100%) 16.1 Lopinavir-ritonavir (400 mg and 100 mg twice daily for 14 days); standard care

Mortality; mechanical ventilation; viral clearance; duration of hospital stay; intensive care unit length of stay; duration of ventilation; time to symptom or clinical

improvement

Cao 202038‡ Published, ChiCTR-OPN-2000029580

43 China 63.0 58.5 Inpatient; coronary artery disease (7.3%); diabetes (19.5%); hypertension (39.0%)

Severe (100%) 12.2 Ruxolitinib (5 mg twice daily); placebo

Mortality; mechanical ventilation; duration of hospital stay; duration of ventilation; time to symptom or clinical improvement; time to viral clearance

Castillo 2020‡; Pilot Covidiol55

Published, NCT04366908 76 Spain 53.0 59.2 Inpatient; cardiovascular disease (4.0%); diabetes (10.5%); hypertension (34.2%); previous lung disease (7.9%)

NR NR Calcifediol (0.532 mg on day 1, then 0.266 mg on day 3 and 7, and then weekly until discharge or ICU admission); standard care

Mortality; adverse events leading to discontinuation

Cavalcanti, 2020107 Published, NCT04322123 667 Brazil 50.3 58.4 Inpatient; intensive care (13.8%); heart failure (1.5%); diabetes (19.1%); hypertension (38.3%); asthma (6.0%); chronic obstructive pulmonary disease (1.8%)

Mild/Moderate (100%)

0 Hydroxychloroquine (400 mg twice daily for 7 days); hydroxychloroquine (400 mg twice daily for 7 days), azithromycin (500 mg/day for 7 days); standard care


Chen 202052 Preprint, ChiCTR2000029559 62 China 44.7 46.8 Inpatient; NR Mild/moderate

(100%)

NR Hydroxychloroquine (200 mg twice daily for 5 days); standard care

Time to symptom or clinical improvement

Chen 202039 Preprint, ChiCTR2000030254 240 China NR 46.6 NR; diabetes (11.4%);

hypertension (28.0%)

Mild/moderate (88.6%); severe (10.2%); critical (1.3%)

NR Favipiravir (600 mg twice daily for 7 days); umifenovir (200 mg three times daily for 7 days)


Chen 2020108 Published, NCT04261517 30 China 48.6 70.0 Inpatient;

diabetes (6.7%); hypertension (26.7%); chronic obstructive pulmonary disease (3.3%)


NR Hydroxychloroquine (400 mg/day for 5 days); standard care

Mortality; adverse events leading to discontinuation; viral clearance; time to symptom or clinical improvement; time to viral clearance

Chen 202052 Preprint,

ChiCTR2000030054

48 China 46.9 45.8 Inpatient; diabetes (18.8%); hypertension (16.7%)


NR Chloroquine (500 mg/day for 10 days); hydroxychloroquine (200 mg twice daily for 10 days); standard care

Mortality; adverse events leading to discontinuation; viral clearance; duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Chen 2020109 Preprint, NCT04384380 33 Taiwan 32.9 57.6 Inpatient Mild/Moderate

(100%)

0 Hydroxychloroquine (200 mg twice daily for 7 days); standard care

Mortality; time to viral clearance

Cheng 2020110 Published, ChiCTR2000030007 200 China 45.0 56.0 Inpatient Critical (0%) 27.0 Granulocyte colony-stimulating factor (5 µg/kg/day for 3 days); standard care

Mortality; mechanical ventilation; viral clearance; duration of hospital stay; time to symptom or clinical improvement

Corral-Gudino 2020; GLUCOCOVID51

Preprint, 2020-001934-37 63 Spain 69.8 61.9 Inpatient; heart disease (12.7%); diabetes (17.5%); hypertension (47.6%); respiratory condition (7.9%)

Critical (0%) 0 Methylprednisolone (40 mg twice daily for 3 days, then 20 mg twice daily for 3 days); standard care

Mortality; mechanical ventilation

Cruz 2020‡; ATENEA-Co-300111

Preprint, IG/CIGB300I/CV/2001 20 Cuba 45.4 70.0 Inpatient; hypertension (25.0%)

Mild/moderate (90%); severe (10%)

NR Anti-CK2 synthetic peptide (2.5 mg/kg/day for 5 days); standard care

Time to viral clearance

Dabbous 2020112 Preprint, NCT04349241 100 Egypt 36.4 50.0 Inpatient Mild/moderate (100%)

NR Favipiravir (600 mg twice daily for 10 days); standard care

Mortality; viral clearance; duration of hospital stay; time to viral clearance

Davoodi 2020113‡ Published, IRCT2019072704434N1

60 Iran 57.7 59.3 Outpatient; diabetes (27.8%); lung disease (1.9%)

Mild/Moderate (100%)

0 Febuxostat (80 mg/day for 5 days); hydroxychloroquine (200 mg twice daily for 5 days)

Mortality; admission to hospital

Davoudi-Monfared 202042 95

Published, IRCT20100228003449N28

92 Iran 58.7 54.3 Inpatient; ischemic heart disease (28.4%); diabetes (27.2%); hypertension (38.3%); asthma (1.2%); chronic obstructive pulmonary disease (1.2%)

Severe (100%) 29.6 Interferon beta-1a (44 μg/ml three times weekly for 14 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; intensive care unit length of stay; duration of ventilation; time to symptom or clinical improvement

de Alencar 202099‡ Published, U1111-1250-356 140 Brazil 58.5 59.3 Inpatient; diabetes (37.8%); hypertension (46.7%)

Severe (100%) 0.7 N-acetylcysteine (14 g in the first 4 hours, then 7 g in the next 16 hours); placebo

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; intensive care unit length of stay

Deftereos 2020; GRECCO-19114

Published, NCT04326790 110 Greece 64.0 58.1 Inpatient; atrial fibrillation (10.5%); coronary artery disease (13.3%); valvulopathy (4.8%); diabetes (20.0%); hypertension (44.8%); chronic obstructive pulmonary disease (4.8%)

NR 2.9 Colchicine (0.5 mg twice daily for 21 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay

Delgado-Enciso 2020‡; TX-COVID19115

Preprint, RPCEC00000309 84 Mexica 47.1 53.6 Outpatient; diabetes (11.9%); hypertension (19.1%); asthma (6.0%)


NR Electrolyzed saline (15 ml/day for 7 days with successive increases up to 30 ml/day if indicated); standard care

Mortality; admission to hospital; adverse events leading to discontinuation

Dequin 2020; CAPE COVID56

Published, NCT02517489 149 France 62.2 69.8 Inpatient; intensive care (100%); cerebrovascular disease (4.0%); diabetes (18.1%); asthma (3.4%); chronic obstructive pulmonary disease (4.0%)

Critical (100%) 81.2 Hydrocortisone (200 mg/day for 7 days, followed by 100 mg once daily for 4 days, and 50 mg once daily for 3 days)


Doi 2020* 82 Published, jRCTs041190120 89 Japan 50.0 61.4 Inpatient NR NR Favipiravir (800 mg twice daily for 10 days starting on day 1 of enrolment); favipiravir (800 mg twice daily for 10 days starting on day 6 of enrolment)

Mortality; mechanical ventilation; viral clearance; duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Duarte 202082‡ Preprint, NCT04355936 82 Argentina 61.9 61.5 Inpatient; stroke (7.7%); diabetes (11.5%); hypertension (30.8%); asthma (1.3%); chronic obstructive pulmonary disease (11.5%)

NR 0 Telmisartan (80 mg twice daily for 14 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay

Edalatifard 2020116 Published, IRCT20200404046947N1

68 Iran 58.5 62.9 Inpatient; cardiovascular disease (17.7%); diabetes (35.5%); hypertension (32.3%); respiratory condition (9.7%)

Severe (100%) 37.1 Methylprednisolone (250 mg/day for 3 days); standard care

Mortality; mechanical ventilation; time to symptom or clinical improvement

Esquivel-Moynelo 2020; ESPERANZA71

Preprint, RPCEC00000307 79 Cuba 38.0 54.0 Inpatient; cardiac disease (6.4%); diabetes (4.8%); hypertension (22.2%);


NR Interferon gamma (0.5 MIU twice a week for 14 days); standard care

Mortality; viral clearance; time to viral clearance

asthma (6.4%)

Farahani 202085* Preprint, IRCT20200406046963N1

29 Iran 64.0 65.5 Inpatient Mild/moderate (0%)

NR Methylprednisolone (1000 mg/day for 3 days), prednisolone (1 mg/kg with tapering of dose over 10 days); standard care

Mortality; mechanical ventilation; intensive care unit length of stay

Fu 2020117 Published, ChiCTR2000030262



NR Interferon kappa (2 mg/day for 6 days), trefoil factor 2 (5 mg/day for 6 days); standard care

Mortality; adverse events leading to discontinuation; viral clearance; duration of hospital stay; time to viral clearance

Furtado 2020; COALITION II118

Published, NCT04321278 447 Brazil 60.2 64.0 Inpatient; heart failure (5.6%); previous stroke (4.0%); previous myocardial infarction (4.5%); diabetes (38.0%); hypertension (60.9%); chronic obstructive pulmonary disease (6.7%)

Severe (100%) 50.3 Azithromycin (500 mg/day for 10 days); standard care

Mortality; mechanical ventilation; duration of hospital stay; ventilator-free days

Goldman 202043* Published, NCT04292899 402 USA, Italy, Spain, Germany, Hong Kong, Singapore, South Korea, Taiwan

61.5 63.7 Inpatient; diabetes (22.7%); hypertension (49.9%); asthma (12.3%)

Severe (100%) 30.7 Remdesivir (100 mg/day for 5 days); remdesivir (100 mg/day for 10 days)

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; time to symptom or clinical improvement

Guvenmez 202089 Published 24 Turkey 58.8 62.5 Inpatient; NR NR 0 Lincomycin (600 mg twice daily

for 5 days); azithromycin (250 mg/day for 5 days)

Viral clearance

Horby 2020; RECOVERY50 92

Published, NCT04381936 6425 UK 66.2 63.6 Inpatient; heart disease (27.3%); diabetes (24.1%); chronic lung disease (21.0%); tuberculosis (0.4%)

NR 15.7 Dexamethasone (6 mg/day for 10 days); standard care


Horby 2020; RECOVERY96 119


NR 16.8 Hydroxychloroquine (400 mg twice daily for 10 days); standard care


Horby 2020; RECOVERY97


NR 4.1 Lopinavir-ritonavir (400 mg and 100 mg twice daily for 10 days); standard care


Hu 2020120‡ Published, ChiCTR2000030058 10 China 54.9 30.0 Inpatient; hypertension (10.0%); chronic obstructive pulmonary disease (10.0%)


0 Leflunomide (20 mg/day for 10 days); standard care

Mortality; viral clearance; time to symptom or clinical improvement; time to viral clearance

Huang 202077 Published, ChiCTR2000029542 22 China 44.0 59.1 Inpatient; cerebrovascular disease (4.5%); diabetes (9.1%); hypertension (18.2%)

Mild/moderate (63.6%); severe (36.4%)

NR Chloroquine (500 mg twice daily for 10 days); lopinavir-ritonavir (400 mg and 100 mg twice daily for 10 days)

Viral clearance; duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Huang 202093‡ Published, ChiCTR2000029387 101 China 42.5 45.5 Inpatient Mild/moderate (100%)

NR Ribavirin (400-600 mg three times daily for 14 days), interferon-alfa (5 mg twice daily for 14 days); lopinavir-ritonavir (400 mg and 100 mg twice daily for 14 days), interferon-alfa (5 mg twice daily for 14 days); ribavirin (400-600 mg three times daily for 14 days), lopinavir-ritonavir (400 mg and 100 mg twice daily for 14 days), interferon-alfa (5 mg twice daily for 14 days)

Mortality; adverse events leading to discontinuation; viral clearance; duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Hung 202044‡ Published, NCT04276688 127 China 51.3 53.5 Inpatient; coronary artery disease (7.9%); cerebrovascular disease (1.6%);


0 Lopinavir-ritonavir (400 mg and 100 mg twice daily for 14 days), ribavarin (400 mg twice daily for 14 days), interferon

Mortality; mechanical ventilation; adverse effects leading to discontinuation;

diabetes (13.4%); hypertension (28.4%); obstructive sleep apnoea (1.6%); tuberculosis (1.6%)

beta-1b (1-3 mlL every other day); lopinavir-ritonavir (400 mg and 100 mg twice daily for 14 days)

duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Ivaschenko 202072 73

Published, NCT04434248 60 Russia 50.7 50.0 Inpatient; NR Mild/moderate (100%)

0 Favipiravir (600 mg twice daily for 14 days); favipiravir (800 mg twice daily for 14 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; viral clearance; time to symptom or clinical improvement

Jeronimo 2020; Metcovid59

Published, NCT04343729 416 Brazil 55.0 65.3 Inpatient; intensive care (35.4%); heart disease (6.6%); diabetes (29.1%); hypertension (48.4%); asthma (2.4%); chronic obstructive pulmonary disease (0.5%); tuberculosis (2.1%)

NR 33.9 Methylprednisolone (0.5 mg/kg twice daily for 5 days); placebo

Mortality; mechanical ventilation; viral clearance; duration of hospital stay

Kimura 2020121‡ Published, NCT0447538 54 USA 38.2 53.3 Outpatient; heart disease (4.4%); diabetes (6.7%); hypertension (24.4%); chronic lung disease (15.6%)

NR NR Hypertonic saline (250 ml twice daily); hypertonic saline with surfactant (250 ml and 2.5 mg twice daily); standard care

Time to symptom or clinical improvement

Lemos 2020‡; HESACOVID100

Published, REBEC RBR-949z6v

20 Brazil 56.5 80.0 Inpatient; cardiovascular disease (10.0%); diabetes (35.0%); hypertension (35.0%)

Critical (100%) 100 Enoxaparin (1 mg/kg/day to 1 mg/kg twice daily for 14 days based on age and creatinine clearance; maximum dose was 140 mg twice daily); standard care

Mortality; venous thromboembolism; clinically-important bleeding; duration of hospital stay; ventilator-free days

Li 2020; ELACOI48 53

Published, NCT04252885 86 China 49.4 46.5 Inpatient; cardiovascular disease (2.3%); diabetes (2.3%); hypertension (10.5%)


0 Lopinavir-ritonavir (200 mg and 50 mg twice daily for 7 to 14 days); umifenovir (200 mg three times daily for 7 to 14 days); standard care

Mortality; adverse effects leading to discontinuation; viral clearance; time to viral clearance

Li 202060* Preprint, ChiCTR2000029638

96 China 53.6 46.8 Inpatient; cerebrovascular disease (5.3%); heart disease (7.5%); diabetes (9.6%); hypertension (19.2%); chronic obstructive pulmonary disease (1.1%); tuberculosis (3.2%)


25.5 Recombinant super-combinant interferon (12 MIU twice daily for 28 days); interferon alpha (5 MIU twice daily for 28 days)

Mortality; mechanical ventilation; adverse events leading to discontinuation; viral clearance; duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Li 2020122‡ Published, NCT04273763 18 China 52.0 77.8 Inpatient; diabetes (11.1%); hypertension (33.3%)


0 Bromhexine hydrochloride (32 mg three times daily for 14 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; time to symptom or clinical improvement

Lopes 2020 61 Preprint, RBR-8jyhxh 38 Brazil 50.8 40.0 Inpatient; cardiovascular disease (40.0%); diabetes (31.4%); respiratory condition (14.3%)

Critical (0%) 2.6 Colchicine (0.5 mg three times daily for 5 days, then 0.5 mg twice daily for 5 days); placebo

Mortality; adverse events leading to discontinuation; duration of hospital stay; intensive care unit length of stay

Lou 202045 Preprint, ChiCTR2000029544 30 China 52.5 72.4 Inpatient;

cardiovascular disease (13.8%); diabetes (6.9%); hypertension (20.7%)

NR 0 Baloxavir marboxil (80 mg/day for up to 3 doses on days 1, 4, and 7); favipiravir (600 mg three times daily for 14 days); standard care

Mortality; mechanical ventilation; viral clearance; time to symptom or clinical improvement; time to viral clearance

Lyngbakken 2020*; NO COVID-1981

Published, NCT04316377 53 Norway 62.0 66.0 Inpatient; coronary heart disease (9.4%); diabetes (17.0%); hypertension (32.1%); chronic obstructive pulmonary disease or asthma (26.4%)

Mild/moderate (0%)

0 Hydroxychloroquine (400 mg twice daily for 7 days); standard care

NA

Mansour 202062‡

Preprint, U1111-1250-1843 60 Brazil 51.6 53.3 Inpatient; diabetes (46.7%); hypertension (50.0%); asthma (3.3%)

Severe (100%) NR Icatibant (30 mg three times daily for 4 days); C1 esterase/kallikrein inhibitor (20 IU/kg on day 1 and 4); standard care

Mortality; duration of hospital stay; intensive care unit length of stay

Mehboob 202075‡

Preprint, NCT04468646 18 Pakistan 53.3 61.1 Inpatient; carotid artery bypass grafting (5.6%); ischemic heart disease (33.3%); diabetes (38.9%); hypertension (50.0%)


NR Aprepitant (80 mg/day for 3-5 days); standard care

Mortality

Miller 2020‡; CARDEA63

Published, NCT04345614 30 USA 59.3 46.7 Inpatient; diabetes (40.0%); hypertension (46.7%)

Severe (86.7%); critical (13.3%)

13.3 Auxora (1.6 mg/kg given in 4 hours for 3 days); standard care

Mortality; mechanical ventilation; time to symptom or clinical improvement

Mitja 202083† Published, NCT04304053 353 Spain 41.6 31.4 Outpatient; cardiovascular disease (12.0%); respiratory condition (5.8%)


0 Hydroxychloroquine (400 mg/day for 7 days); standard care

Mortality; mechanical ventilation; admission to hospital; time to symptom or clinical improvement

Mitja 2020†; BCN PEP-CoV-284

Preprint, NCT04304053 352 Spain 42.0 29.0 Outpatient; NR Mild/moderate (100%)

0 Hydroxychloroquine (400 mg/day for 7 days), cobicistat-boosted darunavir (800 mg/150 mg/day for 7 days); standard care

Mortality; mechanical ventilation; admission to hospital; time to symptom or clinical improvement

Nojomi 2020123‡ Preprint, IRCT20180725040596N2

100 Iran 56.4 60.0 Inpatient; coronary heart disease (9.0%); diabetes (28.0%); hypertension (39.0%); asthma (2.0%)


NR Hydroxychloroquine (400 mg/ day for 1 day), lopinavir-ritonavir (400 mg twice daily for up to 14 days); hydroxychloroquine (400 mg twice daily for 7 to 14 days), umifenovir (200 mg three times daily for 7 to 14 days)

Mortality; mechanical ventilation; duration of hospital stay; time to symptom or clinical improvement

Pan 2020; SOLIDARITY124

Preprint, NCT04315948 5475 Albania, Argentina, Austria, Belgium, Brazil, Canada, Colombia, Egypt, Finland, France, Honduras, India, Indonesia, Iran, Ireland, Italy, Kuwait, Lebanon, Lithuania, Luxembourg, Macedonia, Malaysia, Norway, Pakistan, Phillipines, Peru, Saudi Arabia, South Africa, Spain, Switzerland

NR 62.9 Inpatient; heart disease (20.9%); diabetes (25.2%); asthma (5.1%); chronic lung disease (5.4%)

NR 8.9 Remdesivir (100 mg/day for 10 days); standard care


1854 59.9 Inpatient; heart disease (20.9%); diabetes (21.8%); asthma (4.7%); chronic lung disease (6.9%)

9.0 Hydroxychloroquine (200 mg twice daily for 10 days); standard care



8.2 Lopinavir-ritonavir (200 mg and 50 mg twice daily for 14 days); standard care



6.6 Interferon beta-1a (44 µg three times daily for 6 days; patients on high-flow oxygen, ventilators, or ECMO were given 10 µg/day for 6 days); standard care


Rahmani 202064 Published, IRCT20100228003449N27

80 Iran 60.5 59.1 Inpatient; ischemic heart disease (30.0%); diabetes (31.8%); hypertension (56.1%); asthma (4.6%); chronic obstructive pulmonary disease (4.6%)

Severe (100%) 1.5 Interferon beta-1b (250 μg every other day for 14 days); standard care

Mortality; mechanical ventilation; duration of hospital stay; intensive care unit length of stay; time to symptom or clinical improvement

Ren 2020125‡ Published, ChiCTR2000029853 20 China 52.0 60.0 Inpatient; cardiovascular disease (5.0%); diabetes


0 Azvudine (5 mg/day until discharge); standard care

Mortality; adverse events leading to discontinuation; viral

(5.0%); hypertension (5.0%)

clearance; duration of hospital stay; time to viral clearance

Rosas 2020; COVACTA80

Preprint, NCT04320615 452 Canada, Denmark, France, Germany, Italy, Netherlands, Spain, UK, USA

60.8 69.9 Inpatient; intensive care (56.4%); cardiovascular impairment (28.1%); diabetes (38.1%); hypertension (62.1%); chronic lung disease (16.2%)

Severe (100%) 37.7 Tocilizumab (8 mg/kg, max 800 mg up to two times in 24 hours); placebo

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; intensive care unit length of stay; ventilator-free days; time to symptom or clinical improvement

Sadeghi 202065‡ Published, IRCT20200128046294N2

70 Iran 58.0 51.5 Inpatient; heart failure (15.2%); diabetes (42.4%); hypertension (34.9%); asthma (3.0%); chronic pulmonary disease (22.7%)

Mild/moderate (0%)

0 Sofosbuvir-declatasvir (400 mg and 60 mg once daily for 14 days); standard care

Mortality; mechanical ventilation; duration of hospital stay; time to symptom or clinical improvement

Salehzadeh 2020126

Preprint, IRCT20200418047126N1

100 Iran 56.1 41.0 Inpatient; ischemic heart disease (15.0%); diabetes (11.0%); hypertension (11.0%); chronic obstructive pulmonary disease (4.0%)

NR NR Colchicine (1 mg/day for 6 days); placebo

Mortality; duration of hospital stay

Sekhavati 202066 Published 111 Iran 57.1 46.0 Inpatient NR NR Azithromycin (500 mg/day for 5 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; intensive care unit length of stay

Silva Borba 2020*; CloroCOVID-1935

127

Published, NCT04323527 81 Brazil 51.1 75.3 Inpatient; intensive care (45.7%); cardiovascular disease (9.1%); diabetes (25.5%); hypertension (45.5%); asthma

Severe (100%) NR Chloroquine (600 mg twice daily for 10 days); chloroquine (450 mg/day for 5 days)

Mortality

(7.4%); tuberculosis (3.6%)

Skipper 202098 Published, NCT04308668 491 USA,

Canada

40.0 45.8 Outpatient; cardiovascular disease (1.2%); diabetes (3.9%); hypertension (11.0%); asthma (10.4%); chronic lung disease (0.4%)


0 Hydroxychloroquine (600 mg/day for 5 days); placebo

Mortality; admission to hospital

Spinner 202067 Published, NCT04292730 596 France, Germany, Hong Kong, Italy, Netherlands, Korea, Singapore, Spain, Switzerland, Taiwan, UK, USA

57.0 61.1 Inpatient; cardiovascular disease (56.3%); diabetes (39.7%); hypertension (42.5%); asthma (13.9%)


0.9 Remdesivir (100 mg/day for 10 days); remdesivir (100 mg/day for 5 days); standard care

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; time to symptom or clinical improvement

Sterne 2020; DEXA-COVID 1958

Data from meta-analysis, NCT04325061

19 Spain 60.7 68.4 Inpatient; NR Critical (100%) 100 Dexamethasone (20 mg/day for 5 days, then 10 mg/day for 5 days); standard care

Mortality

Sterne 2020; COVID STEROID58


29 Denmark 59.4 79.3 Inpatient; NR Critical (100%) 51.7 Hydrocortisone (200 mg/day for 7 days); placebo

Mortality

Sterne 2020; Steroids-SARI58


47 China 64.6 74.5 Inpatient; NR Critical (100%) 57.5 Methylprednisolone (40 mg twice daily for 5 days); standard care

Mortality

Sun 202079‡ Published 66 China 49.5 66.7 Inpatient; cardiovascular disease (1.5%); diabetes (9.1%); hypertension (18.2%)


NR Diammonium glycyrrhizinate (150 mg three times daily)

Mortality

Tang 202047 128 Published, ChiCTR2000029868 150 China 46.1 55.0 Inpatient; diabetes (14.0%); hypertension (6.0%)


NR Hydroxychloroquine (800 mg/day for 14 to 21 days); standard care

Mortality; adverse effects leading to discontinuation; viral

clearance; time to symptom or clinical improvement; time to viral clearance

Tomazini 2020; CoDEX68

Published, NCT04327401 299 Brazil 61.4 62.5 Inpatient; intensive care (100%); heart failure (7.7%); diabetes (42.1%); hypertension (66.2%)

Critical (100%) 100 Dexamethasone (20 mg/day for 5 days, then 10 mg/day for 5 days); standard care

Mortality; mechanical ventilation; ventilator-free days; duration of ventilation

Ulrich 2020; TEACH129

Published, NCT04369742 128 USA 66.2 59.4 Inpatient; non-hypertensive cardiovascular disease (25.6%); diabetes (32.0%); hypertension (57.8%); asthma (15.6%); chronic obstructive pulmonary disease (7.0%)

Mild/moderate (0%)

1.56 Hydroxychloroquine (200 mg twice daily for 5 days); placebo

Mortality; mechanical ventilation; viral clearance; duration of hospital stay

Vlaar 2020‡; PANAMO76

Published, NCT04333420 30 Netherlands 60.5 73.3 Inpatient; intensive care (60.0%); diabetes (26.7%); hypertension (30.0%)

Severe (100%) 60.0 IFX-1 (800 mg/day for up to 7 times within 22 days); standard care


Wang 202033 Published, NCT04257656 237 China 65.0 59.3 Inpatient;

cardiovascular disease (7.2%); diabetes (23.7%); hypertension (43.2%)

Severe (100%) 16.1 Remdesivir (100 mg/day for 10 days); placebo

Mortality; mechanical ventilation; adverse events leading to discontinuation; viral clearance; duration of hospital stay; duration of ventilation; time to symptom or clinical improvement

Wang 202086* Published 20 China 47.0 45.0 Inpatient; NR Mild/moderate (100%)

NR Vitamin C (10 g/60 kg twice daily); standard care

NA

Wang 202088* Published 60 China NR 38.3 Inpatient; NR Mild/moderate (100%)

NR Lopinavir-ritonavir (2 tablets twice daily); standard care

NA

Wang 202069 Preprint, ChiCTR2000029765



15.4 Tocilizumab (400 mg for up to two times in 24 hours); standard care

Duration of hospital stay; time to symptom or clinical improvement; time to viral clearance

Wang 2020130‡ Published, ChiCTR2000030058

50 China 55.8 45.8 Inpatient; coronary artery disease (2.1%); diabetes (4.2%); hypertension (25.0%); chronic obstructive pulmonary disease (4.2%)


NR Leflunomide (50 mg twice daily for 1.5 days, then 20 mg/day for 8 days); standard care

Mortality; adverse events leading to discontinuation; viral clearance; duration of hospital stay; time to viral clearance

Wu 2020131‡ Published, ChiCTR20000300001

52 China 58.0 50.0 Inpatient; cardiovascular disease (15.4%); cerebrovascular disease (7.7%); diabetes (15.4%); hypertension (28.8%); chronic obstructive pulmonary disease (5.8%)

NR NR Triazavirin (250 mg three times daily for 7 days in mildly ill patients, 250 mg four times daily for 7 days in severe or critically ill patients); placebo

Mortality; adverse events leading to discontinuation; viral clearance; time to symptom or clinical improvement; time to viral clearance

Yethindra 202091 Published 30 Kyrgyzstan 36.5 60.0 Inpatient; cardiovascular disease (0%)


NR Umifenovir (200 mg three times daily for 1 to 5 days); standard care


Yuan 202087* Preprint, ChiCTR2000029431

21 China 61.0 42.9 Inpatient; NR Mild/moderate (100%)

NR 99mTC-methylene diphosphate (5 ml/day for 7 days); standard care

NA

Zhang 202078‡ Preprint, NCT04264533

56 China 67.4 66.7 Inpatient; intensive care (100%); coronary heart disease (22.2%); diabetes (29.6%); hypertension (44.4%); chronic lung disease (5.6%)

Mild/moderate (0%)

100 Vitamin C (12 g/50 ml given at 12 ml/hour for 7 days); placebo

Mortality; mechanical ventilation; adverse events leading to discontinuation; duration of hospital stay; intensive care unit length of stay; ventilator-free days; duration of

ventilation

Zhao 2020132‡ Published, NCT04310228 26 China 73.5 53.9 Inpatient; coronary artery disease (23.1%); diabetes (11.5%); hypertension (42.3%)


3.9 Favipiravir (600 mg twice daily for 7 days); tocilizumab (4-8 mg/kg in 100 ml for 1 hr); favipiravir (600 mg twice daily for 7 days), tocilizumab (4-8 mg/kg in 100 ml for 1 hr)


Zheng 202049 94‡ Published, ChiCTR2000029496 89 China 46.7 47.2 Inpatient; chronic bronchitis (2.0%)


NR Novaferon (20 μg twice daily for 7 to 10 days); novaferon, lopinavir-ritonavir (200 mg and 50 mg twice daily for 7 to 10 days); lopinavir-ritonavir (200 mg and 50 mg twice daily for 7 to 10 days)

Adverse events leading to discontinuation; viral clearance; time to viral clearance

Zhong 202046‡ Preprint, ChiCTR2000029851 17 China 63.0 76.5 Inpatient; cardiovascular disease (5.9%); diabetes (23.5%); hypertension (47.1%)

Critical (100%) 94.1 Alpha lipoic acid (1200 mg/day for 7 days); placebo


Zhou 202090‡ Published 104 China 52.1 57.7 Inpatient Mild/moderate (100%)

NR Diammonium glycyrrhizinate (150 mg three times daily for 14 days), lopinavir-ritonavir (500 mg twice daily for 14 days); lopinavir-ritonavir (500 mg twice daily for 14 days)

Adverse events leading to discontinuation

NR=not reported NA=not applicable *Not eligible to be included in the network meta-analysis. †Not included in the current iteration of the network meta-analysis but will be included in a future iteration. ‡This study was part of a treatment node with less than 100 participants or less than 20 events.

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111. Cruz LR, Baladron I, Rittoles A, et al. Treatment with an Anti-CK2 Synthetic Peptide Improves Clinical Response in Covid-19 Patients with Pneumonia. A Randomized and Controlled Clinical Trial (preprint). medRxiv 2020:2020.09.03.20187112. doi: 10.1101/2020.09.03.20187112

112. Hany M Dabbous MHE-S, Gihan El Assal et al. . A Randomized Controlled Study Of Favipiravir Vs Hydroxychloroquine In COVID-19 Management: What Have We Learned So Far? Research Square 2020 doi: https://doi.org/10.21203/rs.3.rs-83677/v1

113. Davoodi L, Abedi SM, Salehifar E, et al. Febuxostat therapy in outpatients with suspected COVID-19: A clinical trial. International journal of clinical practice 2020:e13600. doi: 10.1111/ijcp.13600

114. Deftereos SG, Giannopoulos G, Vrachatis DA, et al. Effect of Colchicine vs Standard Care on Cardiac and Inflammatory Biomarkers and Clinical Outcomes in Patients Hospitalized With Coronavirus Disease 2019: The GRECCO-19 Randomized Clinical Trial. JAMA network open 2020;3(6):e2013136. doi: 10.1001/jamanetworkopen.2020.13136

115. Delgado-Enciso I, Paz-Garcia J, Barajas-Saucedo CE, et al. Patient-Reported Health Outcomes After Treatment of COVID-19 with Nebulized and/or Intravenous Neutral Electrolyzed Saline Combined with Usual Medical Care Versus Usual Medical care

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116. Edalatifard M, Akhtari M, Salehi M, et al. Intravenous methylprednisolone pulse as a treatment for hospitalised severe COVID-19 patients: results from a randomised controlled clinical trial. The European respiratory journal 2020 doi: 10.1183/13993003.02808-2020

117. Fu W, Liu Y, Liu L, et al. An open-label, randomized trial of the combination of IFN-kappa plus TFF2 with standard care in the treatment of patients with moderate COVID-19. EClinicalMedicine 2020;(no pagination)(100547) doi: http://dx.doi.org/10.1016/j.eclinm.2020.100547

118. Furtado RHM, Berwanger O, Fonseca HA, et al. Azithromycin in addition to standard of care versus standard of care alone in the treatment of patients admitted to the hospital with severe COVID-19 in Brazil (COALITION II): a randomised clinical trial. Lancet 2020;04:04. doi: https://dx.doi.org/10.1016/S0140-6736(20)31862-6

119. Horby P, Mafham M, Linsell L, et al. Effect of Hydroxychloroquine in Hospitalized Patients with COVID-19: Preliminary results from a multi-centre, randomized, controlled trial. medRxiv 2020:2020.07.15.20151852. doi: 10.1101/2020.07.15.20151852

120. Hu K, Wang M, Zhao Y, et al. A Small-Scale Medication of Leflunomide as a Treatment of COVID-19 in an Open-Label Blank-Controlled Clinical Trial. Virologica Sinica 2020 doi: 10.1007/s12250-020-00258-7

121. Kimura KS, Freeman MH, Wessinger BC, et al. Interim Analysis of an Open-label Randomized Controlled Trial Evaluating Nasal Irrigations in Non-hospitalized Patients with COVID-19. International Forum of Allergy & Rhinology 2020;n/a(n/a) doi: 10.1002/alr.22703

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131. Wu X, Yu K, Wang Y, et al. Efficacy and safety of triazavirin therapy for coronavirus disease 2019: A pilot randomized controlled trial. Engineering 2020

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As of 27 October 2020,

15130 records identified from literature search

14607 English bibliographic databases and pre-print servers 523 Chinese bibliographic databases and pre-print servers

14506 records excluded for not being relevant

300 full text articles assessed for eligibility

118 randomized trials + 2 meta-analyses of randomized trials included

14806 records after duplicates removed

180 full text articles excluded

65 not a randomized trial 25 randomized trial with no results

10 not exposed to or infected with COVID-19

6 prophylaxis

80 wrong intervention

16 blood product therapies 32 traditional Chinese medicine excluding specific molecules at specific doses 3 exercise/rehabilitation 4 personal protective equipment 3 psychological and educational 13 vaccine 2 oxygen delivery 1 diagnostic imaging 5 other 1 removed from preprint server by study authors

94 publications with 106 unique randomized trials (data from 17 unpublished randomized trials in 2 meta-analyses)

89 English and 5 Chinese texts 64 published and 30 preprints

12 preprints of published trials 10 duplicates 1 correction 2 post-hoc / pooled analyses 1 preliminary report of published final report

85 randomised trials included in this systematic review 21 randomised trials will be included in the upcoming update 43 included in analyses

4 different doses/durations of same drug compared 4 no outcomes of interest reported 2 data from primary authors needed 32 did not meet 100 patient / 20 event threshold for analysis

20 records identified from external sources

19 Epistemonikos COVID-19 Evidence 1 reference list of published study

Figure 1. Study selection

Mortality Mechanical ventilation Adverse events Admission to hospital Viral clearance at 7 daysDuration of

hospitalization ICU length of stayDuration of mechanical

ventilationTime to symptom

resolutionTime to viral

clearance Ventilator free daysStandard care* 130 per 1,000 116 per 1,000 15 per 1,000 43 per 1,000 484 per 1,000 13 days 13 days 15 days 11 days 10 days 11 days Azithromycin 6 (-40 to 62) 1 (-60 to 90) 0.4 (-2.9 to 3.9) -1.7 (-5.1 to 1.8)Colchicine -106 (-129 to 42) -1.6 (-2.8 to -0.3)**Corticosteroids -17 (-34 to 1)*** -29 (-54 to 1)**** 5 (-426 to 458) -0.9 (-3.4 to 1.7) -3.8 (-5.9 to -1.8) -1.4 (-3.4 to 0.62) 2.6 (0.2 to 5.0)Favipiravir 63 (-113 to 773) 81 (-301 to 399)Hydroxychloroquine 11 (-11 to 38)*** 20 (-18 to 76)**** 16 (-11 to 192)** -26 (-38 to 12)** 18 (-293 to 334) 0.1 (-1.9 to 2.0) -2.0 (-4.0 to 0.1) -0.7 (-4.3 to 4.8)**Hydroxychloroquine + azithromycin -48 (-103 to 66) 58 (-32 to 216) 0.6 (-1.2 to 2.4)**Interferon beta 2 (-35 to 35) -13 (-60 to 45)Interferon gamma 436 (-215 to 516)Interferon kappa+ treefoil factor 2 290 (-334 to 503)Lopinavir-ritonavir -12 (-31 to 10) 10 (-31 to 60)**** -235 (-449 to 164) -0.4 (-1.7 to 0.6)** -1.0 (-4.1 to 3.2)rhG-CSF -102 (-124 to -41)*** -96 (-108 to -68) -0.7 (-2.3 to 1.0)** -0.8 (-4.5 to 4.6)Remdesivir -12 (-35 to 14)*** -33 (-65 to 1)**** 0 (-9 to 40) 14 (-429 to 460) -0.2 (-1.9 to 1.2)** -1.3 (-4.1 to 1.5) -2.0 (-4.2 to 0.9)Tocilizumab 5 (-46 to 81) -35 (-80 to 54) -8 (-15 to 300)** -2.5 (-6.9 to 1.8) -4.5 (-13.8 to 4.9) -1.8 (-5.0 to 3.4) 4.7 (-4.2 to 13.9)Umifenovir -130 (-130 to 870)

Most beneficial Intermediate benefit Not different from SC HarmfulHigh/ moderate certaintyLow/ very low certainty

*Numbers presented are absolute risk differences (95% CI) per 1000 patients or mean difference (95% CI) when compared to standard care** The best estimate of effect was obtained from direct evidence*** Fixed effects NMA estimates (vs standard care): Corticosteroids, -18 (-30 to -7); Hydroxychloroquine, 10 (-5 to 29); Lopinavir-Ritonavir, -14 (-26 to 0); Remdesivir, -10 (-26 to 9)**** Fixed effects NMA estimates (vs standard care): Corticosteroids, -57 (-85 to -27); Hydroxychloroquine, 30 (-5 to 66); Lopinavir-Ritonavir, 29 (-3 to 63); Remdesivir, -24 (-51 to 5)Empty cells: there was no evidence for the specific interventionrSG-CSF: Recombinant human granulocyte colony-stimulating factor

Fig 2. Summary of effects compared with standard care

therapeutics and covid-19

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